Deep Learning (CNN, RNN, LSTM) * Transformer architectures and attention mechanisms * Deep experience with Generative AI, including: * Large Language Models (LLMs), Retrieval-Augmented Generation ...
Deep Learning (CNN, RNN, LSTM) * Transformer architectures and attention mechanisms * Deep experience with Generative AI, including: * Large Language Models (LLMs), Retrieval-Augmented Generation ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
Perform machine and deep learning models: neural network model, text classification using LSTM and CNN * Computer visioning modeling using Azure ML * Keras, Python and Torch libraries * Track and ...
Perform machine and deep learning models: neural network model, text classification using LSTM and CNN * Computer visioning modeling using Azure ML * Keras, Python and Torch libraries * Track and ...
Data Science Specialist
Manhattan, NY · On-site
Strong skills in scientific data analyses, modeling, visualization and communication of results ... Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine ...
Data Science Specialist
Manhattan, NY · On-site
Strong skills in scientific data analyses, modeling, visualization and communication of results ... Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
DATA SCIENTIST
Austin, TX · On-site
Strong skills in scientific data analyses, modeling, visualization and communication of results ... Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine ...
DATA SCIENTIST
Austin, TX · On-site
Strong skills in scientific data analyses, modeling, visualization and communication of results ... Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
(USA)Staff, Data Scientist
$143K - $286K/yr
ML approaches (GBMs, Random Forests, linear/elastic models with engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting ...
Strong skills in scientific data analyses, modeling, visualization and communication of results ... Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine ...
Strong skills in scientific data analyses, modeling, visualization and communication of results ... Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine ...
Strong skills in scientific data analyses, modeling, visualization and communication of results ... Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine ...
Strong skills in scientific data analyses, modeling, visualization and communication of results ... Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine ...
Data Scientist
Washington, DC · On-site
... models and/or systems * 1+ year of experience specifically with deep learning (e.g., CNN, RNN, LSTM) * 1+ year of experience building NLP and NLG tools. * Experience with wide range of LLMs (Llama ...
Quick apply
Data Scientist
Washington, DC · On-site
... models and/or systems * 1+ year of experience specifically with deep learning (e.g., CNN, RNN, LSTM) * 1+ year of experience building NLP and NLG tools. * Experience with wide range of LLMs (Llama ...
AI Architect
Nashville, TN · On-site
Deep Learning (CNN, RNN, LSTM) * Transformer architectures and attention mechanisms * Deep experience with Generative AI, including: * Large Language Models (LLMs), Retrieval-Augmented Generation ...
AI Architect
Nashville, TN · On-site
Deep Learning (CNN, RNN, LSTM) * Transformer architectures and attention mechanisms * Deep experience with Generative AI, including: * Large Language Models (LLMs), Retrieval-Augmented Generation ...
Senior Data Scientist - GenAI (Retail)
Chicago, IL · On-site +1
Experience with MLflow and model lifecycle management * Generative AI Knowledge: Solid ... Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.) * Experience with ...
Senior Data Scientist - GenAI (Retail)
Chicago, IL · On-site +1
Experience with MLflow and model lifecycle management * Generative AI Knowledge: Solid ... Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.) * Experience with ...
Senior Software Engineer, Machine Learning
Santa Monica, CA · Remote
$140K - $225K/yr
But the fun doesn't stop at model-building. Our work involves an immersive dive into data ... Short-Term Memory (LSTM), and Generative Adversarial Networks (GANs) * Background with NLP ...
Senior Software Engineer, Machine Learning
Santa Monica, CA · Remote
$140K - $225K/yr
But the fun doesn't stop at model-building. Our work involves an immersive dive into data ... Short-Term Memory (LSTM), and Generative Adversarial Networks (GANs) * Background with NLP ...
... modeling in geospatial, network topography, recommendation-systems, environmental systems and/or ... LSTM,Keras • Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting ...
... modeling in geospatial, network topography, recommendation-systems, environmental systems and/or ... LSTM,Keras • Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting ...
... LSTM networks and transformer-based model, BERT to extract insights from financial datasets; economic and econometrics modeling and data mining, using regression, principal-component analysis, and ...
... LSTM networks and transformer-based model, BERT to extract insights from financial datasets; economic and econometrics modeling and data mining, using regression, principal-component analysis, and ...
Experience with MLflow and model lifecycle management * Generative AI Knowledge: Solid ... Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.) * Experience with ...
Quick apply
Experience with MLflow and model lifecycle management * Generative AI Knowledge: Solid ... Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.) * Experience with ...
Principal Data Scientist (Remote)
Lansing, MI · On-site +1
Understanding of NLP concepts such as topic modeling, Word2Vec, sentiment analysis, OCR, etc. * Knowledge of advanced neural net architectures like LSTM, CNN, Transformers, Graph NN, etc.
Principal Data Scientist (Remote)
Lansing, MI · On-site +1
Understanding of NLP concepts such as topic modeling, Word2Vec, sentiment analysis, OCR, etc. * Knowledge of advanced neural net architectures like LSTM, CNN, Transformers, Graph NN, etc.
... modeling in geospatial, network topography, recommendation-systems, environmental systems and/or ... LSTM,Keras • Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting ...
... modeling in geospatial, network topography, recommendation-systems, environmental systems and/or ... LSTM,Keras • Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting ...
Lstm Model information
What are the key skills and qualifications needed to thrive as an LSTM Model Developer, and why are they important?
To thrive as an LSTM Model Developer, you need a solid background in machine learning, deep learning, and programming, typically supported by a degree in computer science, data science, or a related field. Proficiency with Python, TensorFlow, Keras, and knowledge of time series data handling are crucial, as are relevant certifications in AI or deep learning. Strong analytical thinking, attention to detail, and effective problem-solving set exceptional candidates apart. These skills ensure the development of robust and accurate LSTM models for complex sequence prediction tasks and real-world applications.
What are some common challenges faced when deploying LSTM models in a production environment?
Deploying LSTM models in production often involves challenges such as managing computational resources due to the model's complexity and ensuring low-latency predictions for real-time applications. Additionally, LSTMs can be sensitive to input data format and require careful preprocessing and consistent data pipelines. Monitoring model performance over time is essential, as LSTMs may degrade if the underlying data distribution changes. Collaborating with data engineers and DevOps teams is also key to ensure smooth integration and scalability.
What is the difference between Lstm Model vs Data Scientist?
| Aspect | Lstm Model | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of machine learning, deep learning, programming (Python, TensorFlow) | Statistics, programming, data analysis, often a degree in related fields |
| Work Environment | Developing models, coding, testing algorithms | Data analysis, reporting, collaborating with teams |
| Industry Usage | AI, NLP, time-series forecasting | Business analytics, research, data-driven decision making |
While an Lstm Model is a specific deep learning technique used for sequence data, a Data Scientist applies various methods, including LSTM, to analyze data and generate insights. The LSTM model is a tool within a Data Scientist's skill set, focusing on model development, whereas Data Scientists handle broader data analysis tasks across industries.
What is an LSTM model?
An LSTM (Long Short-Term Memory) model is a type of recurrent neural network (RNN) architecture used in the field of deep learning. It is specifically designed to learn and remember long-term dependencies in sequential data, making it highly effective for tasks like language modeling, speech recognition, and time series prediction. LSTM models use special memory cells and gating mechanisms to manage and update information over long sequences, which helps them overcome the limitations of traditional RNNs such as the vanishing gradient problem.
Opening for AI Architect with Healthcare || Nashville, TN -Hybrid || Contract
SR Partners LLCNashville, TN
Other
Posted 18 days ago
Job description
Job Title: AI Architect with Healthcare
Location: Nashville, TN-Hybrid
Mode of Hire: Contract
Job Description:
Role Overview:
We are seeking a visionary to lead the design, governance, and implementation of next-generation Generative AI and Agentic Systems across the enterprise. This role is responsible for translating complex business problems into scalable, secure, and production-grade AI solutions, with a strong emphasis on autonomous agents, intelligent workflows, and AI-augmented SDLC ecosystems.
The ideal candidate brings a rare combination of enterprise-scale system architecture expertise, deep Generative AI knowledge, and hands-on engineering leadership, enabling them to operate seamlessly across strategy, design, and execution phases.
Key Responsibilities:
1. Architecture & System Design
- Own the end-to-end architecture of large-scale, distributed GenAI platforms, including microservices, data pipelines, and AI inference layers.
- Define reference architectures and design patterns for Generative AI, agentic workflows, and AI-enabled enterprise platforms.
- Ensure all systems are secure, scalable, fault-tolerant, cost-efficient, and production-ready.
2. Agentic Systems & Workflow Orchestration
- Design and implement autonomous and semi-autonomous multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom orchestration engines.
- Enable agent collaboration, task planning, memory management, tool use, and self-reflection capabilities.
- Architect agent-driven enterprise workflows (e.g., code generation, testing, incident triage, knowledge discovery, and business process automation).
3. Generative Model Engineering
- Lead model selection, fine-tuning, and optimization of Large Language Models (LLMs) and Small Language Models (SLMs), including OpenAI, Anthropic, Gemini, LLaMA, Mistral, and domain-specific models.
- Apply Parameter-Efficient Fine-Tuning (PEFT) techniques such as LoRA, QLoRA, adapters, and distillation to optimize cost and performance.
- Oversee Retrieval-Augmented Generation (RAG) architectures, vector search, prompt engineering, memory augmentation, and evaluation pipelines.
- Drive experimentation with Diffusion models, GANs, and multimodal models where applicable.
4. LLMOps / MLOps & Cloud Infrastructure
- Architect and standardize LLMOps/MLOps pipelines for training, evaluation, deployment, observability, and lifecycle management.
- Design cloud-native AI platforms on AWS, Azure, or Google Cloud Platform, leveraging GPU/TPU infrastructure, Kubernetes, and serverless computing patterns.
- Implement comprehensive monitoring for latency, hallucinations, model drift, cost usage, security events, and SLA compliance.
- Optimize inference using techniques such as quantization, batching, caching, and intelligent model routing.
5. AI-Driven SDLC & Developer Experience
- Architect AI-augmented Software Development Lifecycle (SDLC) systems, including:
- Agentic code generation and refactoring
- Automated test generation and validation
- Intelligent CI/CD workflows
- AI-powered documentation and knowledge management
- Partner with platform and Developer Experience (DevEx) teams to embed AI into developer tooling and workflows.
6. Governance, Security & Responsible AI
- Define AI governance frameworks covering model risk, data privacy, lineage, explainability, bias detection, and regulatory compliance.
- Ensure alignment with security, legal, and regulatory requirements (e.g., HIPAA, SOC2, GDPR, as applicable).
- Establish robust guardrails for safe agent behavior, access control, prompt injection defense, and data leakage prevention.
7. Strategy, Leadership & Collaboration
- Serve as a technical thought leader and advisor to executive stakeholders.
- Lead and mentor senior engineers, data scientists, and AI researchers.
- Manage multiple concurrent initiatives while balancing innovation with reliable delivery.
- Drive buy-vs-build decisions, vendor evaluations, and strategic roadmap planning.
- Evangelize AI best practices across engineering, product, and data teams.
Required Qualifications:
Core Engineering & Architecture
- 12+ years of experience in enterprise-grade full-stack or platform architecture.
- Strong background in product engineering, distributed systems, and microservices.
- Demonstrated ability to design mission-critical, high-availability systems.
AI / ML & Generative AI Expertise
- Strong theoretical and hands-on expertise in:
- Deep Learning (CNN, RNN, LSTM)
- Transformer architectures and attention mechanisms
- Deep experience with Generative AI, including:
- Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering
- GANs and Diffusion models
- Proven experience integrating with OpenAI, Azure OpenAI, Hugging Face, or equivalent platforms.
Technical Stack:
- Expert-level proficiency in Python; strong working knowledge of C++ and Java.
- Extensive experience with PyTorch, TensorFlow, and Keras.
- Expertise in designing RESTful APIs, GraphQL, and event-driven architectures using Kafka or RabbitMQ.
- Strong understanding of databases, vector stores, and streaming systems.
Cloud & DevOps:
- Proven track record of deploying and operating large-scale ML/AI workloads in production.
- Hands-on experience with Kubernetes, Docker, and Infrastructure as Code (IaC) tools (Terraform, Bicep, or CloudFormation).
- Familiarity with CI/CD pipelines, observability stacks, and secure cloud networking.
Preferred Other Skills:
- Experience in Healthcare, Payer, or Life Sciences domains, including regulated data environments.
- Exposure to edge AI, on-device inference, or real-time decision-making systems.
- Contributions to open-source AI/ML projects or published technical thought leadership.
- Experience building internal AI platforms or AI Centers of Excellence (CoE).
PSRTEK is a reputed technology recruitment and IT staffing brand with a global footprint and an admired client base. As an ideas and innovation powerhouse with a culture of excellence, we bring remarkable expertise and deliver powerfully transformative results.