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Nlp Engineer Jobs in Connecticut (NOW HIRING)

Prophecy Technologies

AI/ML Engineer

Hartford, CT · On-site

Design, build, and fine-tune NLP/LLM solutions for business use cases (e.g., classification ... Collaborate with data engineering and platform teams on data pipelines, deployments, and CI/CD.

Tiger Analytics Inc.

GenAI Data Engineer

Hartford, CT

$115K - $138K/yr

Instead of just using LLMs, you will integrate AI tools (OCR, NLP entities, Document AI) into the engineering flow to transform unstructured blobs into structured insights. * Tune complex SQL queries ...

Tiger Analytics Inc.

GenAI Data Engineer

Hartford, CT · On-site +1

$115K - $138K/yr

Instead of just using LLMs, you will integrate AI tools (OCR, NLP entities, Document AI) into the engineering flow to transform unstructured blobs into structured insights. * Tune complex SQL queries ...

CVS Health

AI/LLM Engineer

Hartford, CT · On-site

$101K - $203K/yr

Natural Language Processing (NLP) fundamentals * Machine learning concepts (training, evaluation ... Prompt engineering and prompt optimization * Embeddings and vector search * Experience designing ...

UnitedHealth Group

Sr Software Engineer

Hartford, CT · Remote

$91K - $163K/yr

Connecting. Growing together. Position Summary As a Senior Software Engineer within ... Experience integrating Large Language Models (LLMs), natural language processing (NLP), or vector ...

UnitedHealth Group

Sr Software Engineer

Hartford, CT · On-site

$91K - $163K/yr

Connecting. Growing together. Position Summary As a Senior Software Engineer within ... Experience integrating Large Language Models (LLMs), natural language processing (NLP), or vector ...

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Nlp Engineer information

See Connecticut salary details

$34.7K

$102.1K

$130.8K

How much do nlp engineer jobs pay per year?

As of Aug 14, 2026, the average yearly pay for nlp engineer in Connecticut is $102,056.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,200.00 and $129,400.00 per year, depending on experience, location, and employer.

What are typical projects or responsibilities for an NLP engineer in a team setting?

As an NLP Engineer, you will often work on projects such as building text classification models, developing chatbots, or creating tools for information extraction from documents. Your responsibilities may include data preprocessing, model development and evaluation, and collaborating with data scientists or software engineers to deploy models into production. Day-to-day, you’ll also be involved in regular code reviews, experiment tracking, and keeping up with new advancements in NLP techniques. Teamwork and clear communication are essential, as you’ll regularly coordinate with product managers and other stakeholders to align solutions with business goals. This environment provides opportunities to develop both technical depth and leadership skills as you take on increasingly complex projects.

What are the key skills and qualifications needed to thrive in the NLP engineer position, and why are they important?

To thrive as an NLP Engineer, you need a strong foundation in mathematics, programming (especially Python), and experience with machine learning and natural language processing techniques, typically supported by a degree in computer science or a related field. Familiarity with NLP frameworks such as spaCy, NLTK, or Hugging Face Transformers, as well as experience using cloud platforms and version control systems, is also important. Strong problem-solving abilities, communication skills, and a collaborative mindset help you stand out in this role. These skills and qualities are crucial for developing, deploying, and optimizing language-based AI solutions that meet real-world business needs.

What does an NLP engineer do?

An NLP Engineer develops algorithms and models that enable computers to understand, interpret, and generate human language. They work with machine learning, deep learning, and linguistic principles to build applications like chatbots, speech recognition systems, and text analytics tools. Their role involves data preprocessing, model training, and optimization to improve language-based AI solutions.

What are the most commonly searched types of Nlp Engineer jobs in Connecticut?

The most popular types of Nlp Engineer jobs in Connecticut are:

What are popular job titles related to Nlp Engineer jobs in Connecticut?

For Nlp Engineer jobs in Connecticut, the most frequently searched job titles are:

What job categories do people searching Nlp Engineer jobs in Connecticut look for?

The top searched job categories for Nlp Engineer jobs in Connecticut are:

Nlp Engineer jobs near you
Infographic showing various Nlp Engineer job openings in Connecticut as of August 2026, with employment types broken down into 91% Full Time, 4% Part Time, and 5% Contract. Highlights an 83% Physical, 5% Hybrid, and 12% Remote job distribution, with an average salary of $102,056 per year, or $49.1 per hour.

AI/ML Engineer

Prophecy Technologies

Hartford, CT • On-site

Full-time

Re-posted 6 days ago

Job description

Role Overview:
We are seeking an AI/ML Engineer with hands-on experience building, fine-tuning, and deploying LLM-based solutions. This role involves working on Natural Language Processing (NLP) and Generative AI (GenAI) use cases such as classification, summarization, and retrieval-augmented generation (RAG), in partnership with product and engineering teams to deliver scalable, secure, and measurable outcomes.
Key Responsibilities:
  • Design, build, and fine-tune NLP/LLM solutions for business use cases (e.g., classification, summarization, Q&A).
  • Develop efficient, well-documented Python code for training, inference, and evaluation pipelines.
  • Build RAG applications using embeddings, vector databases, and prompt engineering techniques.
  • Integrate LLM applications into services/APIs and ensure performance, reliability, and scalability.
  • Establish model evaluation, monitoring, and governance practices (quality, safety, bias, drift).
  • Collaborate with data engineering and platform teams on data pipelines, deployments, and CI/CD.

Required Skills:
  • 6+ years of overall experience in software development focusing on AI/ML engineering.
  • 2+ years of hands-on experience with deep learning for NLP/GenAI.
  • Strong Python proficiency, including writing production-quality, testable, maintainable code.
  • Experience with deep learning frameworks and libraries: PyTorch or TensorFlow; Hugging Face Transformers.
  • Solid understanding of deep learning architectures and modern NLP/LLM concepts (tokenization, attention/transformers, fine-tuning approaches).
  • Experience building rapid prototypes and APIs using FastAPI/Flask and/or Streamlit.

Qualifications:
  • 6+ years of overall experience in software development focusing on AI/ML engineering.
  • 2+ years of hands-on experience with deep learning for NLP/GenAI.
  • Strong Python proficiency.
  • Experience with PyTorch or TensorFlow; Hugging Face Transformers.
  • Solid understanding of deep learning architectures and modern NLP/LLM concepts.
  • Experience building rapid prototypes and APIs using FastAPI/Flask and/or Streamlit.

Preferred Skills:
  • Experience with LLM orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or similar).
  • Experience with vector databases and embedding workflows (e.g., FAISS, Pinecone, Weaviate, Chroma, Azure AI Search).
  • Experience deploying and scaling ML/LLM workloads on cloud platforms (Azure preferred; GCP/AWS acceptable).
  • Familiarity with agentic architectures and multi-agent patterns (e.g., AutoGen or similar).
  • Healthcare domain knowledge and/or experience building solutions in regulated environments.

Standard Technical Skills:
  • MLOps & Deployment: Model packaging and serving, CI/CD, containers (Docker), orchestration (Kubernetes), experiment tracking (MLflow), model registry, monitoring/observability.
  • LLM Evaluation: Offline/online evaluation, prompt/version management, automated testing, hallucination and factuality checks, retrieval evaluation, human-in-the-loop review.
  • Software Engineering: Git, code reviews, unit/integration testing (pytest), REST APIs, basic system design, performance optimization.
  • Security & Compliance: Secure coding, secrets management, PII/PHI handling, access control; familiarity with responsible AI principles is a plus.

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