Senior AI Engineer

Position: Sr. AI Engineer
Location: Boston – 4 Days/Week (Onsite )

Experience: 12+ Years

Requirements: 

* Need local or Candidates located within a 25-mile radius of the job location are acceptable

* Face-to-Face Interview Required

*  Federal clearance is mandatory.

 Position Overview

We are looking for a Senior AI Engineer with strong expertise in building enterprise-grade AI/ML and Generative AI solutions. The ideal candidate should have hands-on experience with LLMs, RAG architectures, AI agents, prompt engineering, and scalable AI platforms in production environments.

Responsibilities:

* Design, build, and deploy scalable AI/LLM applications, AI agents, and intelligent automation solutions. 

* Develop RAG pipelines, AI assistants, MCP platforms, and integrate AI solutions with APIs, enterprise systems, and cloud environments. 

*  Improve AI model performance using prompt engineering, fine-tuning, orchestration, guardrails, and monitoring/LLMOps practices. 

*  Work closely with business, engineering, and external teams while staying updated on the latest Generative AI and Agentic AI technologies

Required Skills:

* Strong Python programming skills with experience in AI/ML libraries like NumPy, Pandas, SciPy, and Scikit-learn. 

* Hands-on experience developing and deploying production-level LLM and Generative AI applications. 

* Experience working with OpenAI, Anthropic, Google AI, Hugging Face, and open-source LLM models. 

* Good knowledge of LangChain, LlamaIndex, RAG pipelines, vector databases, and AI orchestration frameworks. 

* Experience with prompt engineering, embeddings, model evaluation, APIs, MCPs, and scalable backend development. 

*Familiarity with AI coding tools such as Claude Code, Cursor, Codex, and GitHub Copilot, along with strong communication and teamwork skills. 

Preferred Qualifications:

* Experience building AI agents, autonomous workflows, and agentic AI platforms using frameworks like LangGraph, AutoGen, CrewAI, or Semantic Kernel. 

* Knowledge of LLM fine-tuning techniques such as LoRA and PEFT to improve AI model performance and efficiency. 

* Hands-on experience with cloud platforms like AWS, Azure, or GCP, along with Java/JavaScript-based enterprise application environments. 

* Familiarity with MLOps/LLMOps, AI monitoring, governance, observability, security guardrails, and production AI system management.

Education:

*Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Applied Mathematics, or related field.