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Ml Inference Jobs in Connecticut (NOW HIRING)

AI/ML Development Analyst

Norwalk, CT ยท On-site

$100K - $150K/yr

Job Title AI/ML Development Analyst Job Summary Commonfund is seeking a highly motivated AI/ML ... Develop and maintain data pipelines, model training workflows, and inference services . * Analyze ...

Google AI Lead Architect

Hartford, CT

$55.75 - $76.50/hr

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

Google AI Lead Architect

Stamford, CT

$59 - $80.75/hr

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

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Ml Inference information

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

What are some common challenges faced by ML Inference Engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.
What job categories do people searching Ml Inference jobs in Connecticut look for? The top searched job categories for Ml Inference jobs in Connecticut are:
What cities in Connecticut are hiring for Ml Inference jobs? Cities in Connecticut with the most Ml Inference job openings:
AI/ML Development Analyst

AI/ML Development Analyst

Commonfund

Norwalk, CT โ€ข On-site

$100K - $150K/yr

Full-time

Posted 24 days ago


Job description

Job Title

AI/ML Development Analyst

Job Summary

Commonfund is seeking a highly motivated AI/ML Development Analyst to design, develop, and deploy advanced artificial intelligence and machine learning solutions. The ideal candidate will have strong experience in AI/ML development, Agentic AI systems, Python programming, SQL, and web application development. Recent college grads with this experience through their degree program are welcome to apply.

This role involves building intelligent systems, developing data-driven models, and integrating AI capabilities into scalable applications for business processes at Commonfund. Candidates should have a strong analytical mindset and an interest in applying AI/ML solutions within the financial domain.

Key Responsibilities

  • Design, develop, and deploy machine learning and AI-driven solutions for business and financial applications.
  • Build and implement Agentic AI systems, including autonomous workflows and multi-agent architectures.
  • Develop and maintain data pipelines, model training workflows, and inference services.
  • Analyze large datasets to extract insights and build predictive or decision-support models.
  • Design and implement APIs and web-based interfaces for AI/ML solutions.
  • Write efficient and scalable code using Python and related frameworks.
  • Use SQL to query, transform, and analyze structured data.
  • Collaborate with cross-functional teams including product, engineering, and data teams to integrate AI capabilities into applications.
  • Evaluate new AI/ML techniques, frameworks, and research for potential adoption.
  • Document models, algorithms, and system architectures.
  • Ensure solutions follow best practices for security, scalability, and reliability.

Required Qualifications

  • Bachelorโ€™s or Masterโ€™s degree in Mathematics, Data Science, Computer Science with Artificial Intelligence, or a related field.
  • Strong experience with Python programming.
  • Minimum one year hands-on experience developing machine learning or AI-based solutions.
  • Experience building or working with Agentic AI systems or autonomous AI workflows.
  • Strong knowledge of SQL and database systems.
  • Experience with web development frameworks or API development.
  • Experience with ML frameworks as TensorFlow, PyTorch, or Scikit-learn
  • Strong analytical, problem-solving, and debugging skills.
  • Ability to work independently and collaboratively in a fast-paced environment.

Preferred Qualifications

  • Experience working with financial data and developing financial applications.
  • Experience with LLM integration, prompt engineering, and AI orchestration frameworks.
  • Knowledge of data engineering pipelines and cloud platforms (AWS, Azure).
  • Experience building production-grade AI applications.

Technical Skills

Programming & Development

  • Python
  • SQL
  • REST APIs
  • Web development (e.g., Svelte, FastAPI, or similar)
  • Experience with C# programming and other Microsoft tools preferred.

AI/ML

  • Machine learning model development
  • LLM applications
  • Agentic AI architecture
  • Data analysis and feature engineering

Tools & Platforms (preferred)

  • Git / version control
  • Cloud platforms (AWS, Azure, GCP)
  • Containerization (Docker)
  • Data visualization tools

Key Competencies

  • Analytical thinking and quantitative reasoning
  • Strong problem-solving ability
  • Curiosity and continuous learning in AI technologies
  • Ability to translate business problems into technical AI solutions
  • Interest in applying AI within the financial and investment domain