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Llm Ml Rag Jobs in Springfield, MA (NOW HIRING)

Deloitte

Google AI Lead Architect

Hartford, CT

$55.75 - $76.50/hr

LLM and AI Integration: Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models ... RAG with Vertex AI Search/Vector Search, prompt design, safety policies, observability). * Deep ...

Infosys

AI Engineer

Hartford, CT · On-site

... ML, ML, deep learning or LLM models, and proofs of concepts. • Participate in and deliver ... RAG (Retrieval-Augmented Generation) AI Agents / Agentic frameworks Prompt Engineering Proficiency ...

Infosys Limited Digital

AI Engineer

Hartford, CT · On-site

$115K - $138K/yr

... ML, ML, deep learning or LLM models, and proofs of concepts. • Participate in and deliver ... RAG (Retrieval-Augmented Generation) AI Agents / Agentic frameworks Prompt Engineering Proficiency ...

The Hartford

Applied AI Data Scientist

Hartford, CT · On-site

$90K - $135K/yr

Strong Python and SQL skills; experience with deep learning and ML libraries. * Handson experience building RAG systems, vector search pipelines, and GenAI applications. * Familiarity with modern LLM ...

UnitedHealth Group

Software Engineer

Hartford, CT · Remote

$72K - $130K/yr

Develop and optimize RAG (Retrieval-Augmented Generation) pipelines, including document ingestion ... ML or generative AI solutions in production or enterprise environments * 2 years of experience in ...

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All JobsLlm Ml Rag JobsLlm Ml Rag Jobs in Springfield, MA

Llm Ml Rag information

See Springfield, MA salary details

$44.8K

$75K

$109.6K

How much do llm ml rag jobs pay per year?

As of Jul 3, 2026, the average yearly pay for llm ml rag in Springfield, MA is $75,036.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,800.00 and $86,700.00 per year, depending on experience, location, and employer.

What are some typical challenges faced when working on Retrieval-Augmented Generation (RAG) systems in large language model (LLM) machine learning roles?

Professionals working on LLM ML RAG systems often encounter challenges such as ensuring the accuracy and relevancy of retrieved documents, managing latency for real-time queries, and seamlessly integrating retrieval mechanisms with generation models. Additionally, keeping up with evolving datasets and maintaining high-quality knowledge bases can be demanding. Collaboration with data engineers and domain experts is common to refine retrieval pipelines and optimize the end-to-end system.

What is the difference between Llm Ml Rag vs Data Scientist?

AspectLlm Ml RagData Scientist
Required CredentialsMaster's or PhD in ML, AI, or related fields; certifications in ML frameworksDegree in Computer Science, Statistics, or related; certifications in data analysis or ML
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, product development teams
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting firms
Common Search & ComparisonOften compared for ML specialization and research focusCompared for data analysis, modeling, and business insights

While both roles involve working with machine learning, Llm Ml Rag typically focuses on research and development of large language models, requiring advanced ML expertise. Data Scientists often work on analyzing data, building predictive models, and deriving insights for business decisions. The roles overlap in skills but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as an LLM ML RAG (Retrieval-Augmented Generation) Engineer, and why are they important?

To excel as an LLM ML RAG Engineer, you need a strong background in machine learning, natural language processing, and large language models, typically supported by a degree in computer science or a related field. Proficiency with tools and frameworks like Python, PyTorch/TensorFlow, Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone) is essential, along with experience in deploying and fine-tuning LLMs and integrating retrieval systems. Strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams distinguish top performers in this role. These skills ensure the effective development and deployment of advanced AI solutions that combine generative and retrieval capabilities for high-impact applications.

What are LLM ML RAG jobs?

LLM ML RAG jobs involve working with Large Language Models (LLMs), Machine Learning (ML), and Retrieval-Augmented Generation (RAG) systems. Professionals in these roles typically design, develop, and optimize AI systems that combine language models with retrieval techniques to improve accuracy, relevance, and factual grounding in generated outputs. These jobs often require expertise in natural language processing, deep learning, data engineering, and information retrieval. Key responsibilities might include integrating RAG pipelines, fine-tuning LLMs, and ensuring high-quality responses from AI applications.
What are popular job titles related to Llm Ml Rag jobs in Springfield, MA? For Llm Ml Rag jobs in Springfield, MA, the most frequently searched job titles are:
What job categories do people searching Llm Ml Rag jobs in Springfield, MA look for? The top searched job categories for Llm Ml Rag jobs in Springfield, MA are:
What cities near Springfield, MA are hiring for Llm Ml Rag jobs? Cities near Springfield, MA with the most Llm Ml Rag job openings:
Llm Ml Rag jobs near you
Infographic showing various Llm Ml Rag job openings in Springfield, MA as of June 2026, with employment types broken down into 94% Full Time, 4% Part Time, and 2% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution, with an average salary of $75,036 per year, or $36.1 per hour.
IT - Technology Lead | Enterprise Content Management | IBM Watson

IT - Technology Lead | Enterprise Content Management | IBM Watson

Spruce Infotech

Hartford, CT • On-site

$168K/yr

Full-time

Posted 21 hours ago

Job description

Job Title Technology Lead | Enterprise Content Management | IBM Watson
Work Location & Reporting Address Hartford, CT 6156
Vendor Rate XXX/Hr.
Contract duration 6
Target Start Date 22 Apr 2026
Must Have Skills
Core AI & GenAI Expertise
• Deep experience with Generative AI, LLMs, multi-modal models, RAG systems, and agent-based architectures.
• Strong knowledge of ML algorithms, NLP/NLU techniques, transformers, embeddings, and evaluation frameworks.
Model Tuning & Optimization
• Hands-on expertise with PEFT, LoRA, QLoRA, parameter-efficient fine-tuning, and prompt-tuning strategies.
Frameworks, Tools & Libraries
• Proficiency in:
o LangChain, LangGraph, Pydantic
o FAISS / Chroma / Milvus or other vector DBs
o PyTorch / TensorFlow
o HuggingFace ecosystem
o OpenAI / Azure OpenAI / Claude / Gemini APIs
Full-Stack AI Engineering
• Strong Python engineering skills for building orchestration, pipelines, and backend services.
• Experience deploying AI workloads on Azure/AWS/GCP (or equivalents).
• Understanding of MLOps / AIOps, CI/CD pipelines, containerization, and microservices.
Consultative & Evangelization Skills - Exceptional communication and storytelling abilities.
• Nice to have skills
8-15+ years of experience in AI/ML, with at least 3-5 years in GenAI/LLM-based solutions.
• Master's degree or specialization in Computer Science, AI, ML, Data Science, or related fields.
• Certifications in cloud AI services (Azure AI, AWS ML, GCP Vertex AI) are highly desirable.
Key Responsibilities
1. Strategic AI Leadership & Evangelization - Partner with business and technology leaders to shape the AI roadmap, influence strategy, and embed AI in transformation initiatives.
2. AI Solution Architecture & Full-Stack AI Engineering - Lead design and development of end-to-end AI/GenAI solutions, including data ingestion, model orchestration, inference services, and integration with enterprise systems. Architect multi-model pipelines using platforms and frameworks such as LangChain, LangGraph, Pydantic, vector databases, LLM frameworks, and cloud-native services.
3. Model Development, Tuning & Optimization - Apply advanced model-tuning techniques such as PEFT, LoRA, QLoRA, SFT, and Retrieval-Augmented Generation (RAG).
4. GenAI & ML Engineering Excellence - Build prototype agents, copilots, AI automation flows, and domain-context solutions using modern AI frameworks.
5. Client Engagement & Value Realization - Lead client discussions, articulate solution approaches, drive use case discovery, feasibility assessment, and ROI analysis to prioritize AI initiatives.
Minimum years of experience
8-10 years
Certifications Needed :No
Top 3 responsibilities you would expect the Subcon to shoulder and execute
Solution design
Technical delivery
Team handling
Interview Process (Is face to face required?) No

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