Senior AI Engineer

Job Title : Senior AI Engineer Job Location : Boston MA 02110 Onsite Requirements : Python GenAI tools LLMs Job Description : Responsibilities : Contribute to accurate, unambiguous technical design specifications, including GenAI system integration and AI-enhanced workflows. Deliver high-quality AI-powered software components and services, ensuring adherence to security, performance, longevity, and AI-driven automation best practices. Estimate the size of development tasks in story points, considering LLM inference latency and AI API rate limits. Understand / follow coding conventions, architectures, and best practices for GenAI-powered applications, LLM prompt engineering, and RAG (Retrieval-Augmented Generation) models. Write, debug, and deploy code to production, ensuring timely fixes for GenAI-based APIs, embeddings, and AI-driven microservices. Integrate and optimize OpenAI, Azure OpenAI, Hugging Face, LangChain, and LlamaIndex into enterprise applications. Leverage vector databases (Pinecone, FAISS, ChromaDB) for similarity search and AI retrieval pipelines. Adhere to Definition of Done (DOD) as part of the sprint, including : Unit tests, functional testing LLM performance benchmarking (BLEU, ROUGE, cosine similarity) AI model validation & API response optimization (temperature, top-k, max tokens) Code reviews, bug fixes, documentation Adherence to AI governance & responsible AI practices 30% Contributions to the Team : Learn domain-specific AI applications, including GenAI capabilities in automation, search, and AI-assisted decision-making. Take ownership of AI-enhanced product features, ensuring continuous model improvement and fine-tuning strategies. Contribute to agile ceremonies with a focus on AI-driven solutions and optimizations. Volunteer for GenAI-focused backlog items, such as : RAG model refinement Prompt engineering for better accuracy LLM evaluation and response optimization Participate in scrum meetings (daily stand-ups, sprint planning, readouts, retrospectives) with a focus on AI model iteration and feature scaling. Drive self-organization in AI workflows, ensuring GenAI is used effectively across teams. 10% Cross Functional Coordination and Communication : Work collaboratively across Technology, Product, AI / ML, and DevOps teams to align AI-driven enhancements with business goals. Build strong relationships with AI engineers, data scientists, and cloud architects to optimize LLM-based applications. Ensure AI compliance with security, ethical AI policies, and privacy standards (HIPAA, GDPR, SOC2, AI governance best practices). 10% Mentorship of Others : Train and mentor developers on GenAI integration, AI API usage, embeddings, and vector search optimizations. Guide the team on LLM prompt engineering, RAG model improvements, and API latency optimization. Encourage adoption of AI-enhanced developer workflows (e.g., Copilot, AI-assisted code generation, AI-powered testing). Education, Experience, & Skills Required : 5-10 years of experience in an engineering role, with exposure to AI / ML concepts. Experience in an Agile environment preferred. Bachelor's Degree or equivalent in Computer Science, Engineering, or related field. Strong software engineering experience, including AI model integration and GenAI API workflows. Knowledge of modern programming language : Python (preferred for AI applications). Familiarity with Unix / Linux, Big Data, SQL, NoSQL, and AI data pipelines. Experience with AI frameworks and APIs such as OpenAI GPT, Hugging Face Transformers, LangChain, LlamaIndex. Exposure to retrieval-augmented generation (RAG), embeddings, and AI search optimization techniques. Understanding of vector databases (FAISS, Pinecone, ChromaDB) and similarity search models. Proficiency in cloud-based AI deployments (AWS or Azure OpenAI). Strong grasp of GenAI model evaluation techniques (BLEU, ROUGE, BERT Score, cosine similarity metrics).

• 3rd party and subcontract staffing agencies are not eligible for partnership on this position. 3rd party subcontractors need not apply. This position requires candidates to be eligible to work in the United States, directly for an employer, without sponsorship now or anytime in the future