Senior Software Engineer - AI & Data Engineering Location: Boston, MA (Onsite every Thursday) Role Overview We are seeking a highly skilled Senior Software Engineer with strong expertise in AI and Data Engineering. This position emphasizes hands-on technical execution, contributions to team success, cross-functional collaboration, and mentorship, with a strong focus on Generative AI, LLM integrations, and AI-enhanced workflows.
Responsibilities 50% - Technical Execution - Contribute to clear and precise technical design specifications, focusing on GenAI system integration and AI-driven workflows.
- Deliver high-quality AI-powered components and services while ensuring security, performance, scalability, and automation best practices.
- Estimate development tasks in story points, factoring in LLM inference latency and API rate limits.
- Adhere to coding conventions, architectures, and best practices for GenAI applications, prompt engineering, and RAG models.
- Write, debug, and deploy production-ready code, ensuring quick fixes for GenAI APIs, embeddings, and microservices.
- Integrate and optimize tools/frameworks such as OpenAI, Azure OpenAI, Hugging Face, LangChain, and LlamaIndex.
- Utilize vector databases ( Pinecone, FAISS, ChromaDB) for similarity search and retrieval pipelines.
- Meet sprint "Definition of Done (DOD)" including:
- Unit and functional testing
- LLM benchmarking (BLEU, ROUGE, cosine similarity)
- Model validation & API optimization (temperature, top-k, tokens)
- Code reviews, bug fixes, and documentation
- Responsible AI & governance compliance
30% - Team Contributions - Learn domain-specific AI applications in automation, search, and decision support.
- Own AI-driven product features with ongoing model improvement and fine-tuning.
- Actively engage in agile ceremonies with an AI-first mindset.
- Volunteer for GenAI-related backlog items, such as:
- RAG model refinement
- Prompt engineering enhancements
- LLM evaluation and response tuning
- Participate in scrum (stand-ups, sprint planning, retrospectives) with focus on iterative AI model scaling.
- Promote self-organization and effective GenAI adoption across teams.
10% - Cross-Functional Collaboration - Work closely with Technology, Product, AI/ML, and DevOps teams to align AI solutions with business outcomes.
- Partner with AI engineers, data scientists, and cloud architects to optimize LLM-based solutions.
- Ensure compliance with AI governance, privacy, HIPAA, GDPR, SOC2, and ethical AI standards.
10% - Mentorship & Knowledge Sharing - Train and mentor developers on GenAI integration, API usage, embeddings, and vector search.
- Guide team members in prompt engineering, RAG optimizations, and API latency improvements.
- Encourage use of AI-powered developer workflows (Copilot, AI-driven testing, code generation).
Education, Experience & Skills - 5-10 years in engineering roles with exposure to AI/ML.
- Bachelor's degree (or equivalent) in Computer Science, Engineering, or related field.
- Strong background in software engineering with AI/GenAI model integration.
- Proficiency in Python (preferred for AI work).
- Familiarity with Unix/Linux, Big Data, SQL, NoSQL, and AI pipelines.
- Hands-on experience with OpenAI GPT, Hugging Face Transformers, LangChain, LlamaIndex.
- Exposure to RAG models, embeddings, similarity search, and vector databases (FAISS, Pinecone, ChromaDB).
- Experience deploying AI solutions on AWS or Azure OpenAI.
- Strong knowledge of AI evaluation metrics (BLEU, ROUGE, BERT Score, cosine similarity).
- Agile environment experience preferred.
Behaviors & Abilities - Strong ability to design and implement AI-powered solutions that enhance software functionality.
- Analytical problem-solver skilled in debugging and optimizing AI-generated responses.
- Collaborative mindset to work across engineering, DevOps, and AI/ML functions.
- Deep knowledge of AI-driven feature development, prompt engineering, and embedding optimizations.
- Capable of evaluating AI outputs for bias, accuracy, and compliance.
- Curious, eager to explore new AI models, frameworks, and best practices for scalable GenAI deployment.
