Forward Deployed Engineer - GenAI

We are seeking a skilled software engineer to develop and deploy generative AI applications, including large language model (LLM)-powered features, retrieval-augmented generation (RAG) pipelines, and enterprise search solutions. This role focuses on building production-ready AI capabilities that go beyond prototypes, delivering impactful tools that meet client business needs. The ideal candidate will have hands-on experience with GenAI technologies, strong engineering skills, and the ability to collaborate effectively with cross-functional teams.

Key Responsibilities

- Develop generative AI applications such as LLM-powered features, copilot/chat experiences, and enterprise search solutions.
- Design and implement RAG pipelines, including chunking strategies, embedding selection, hybrid retrieval, re-ranking, and GraphRAG for structured retrieval scenarios.
- Fine-tune and adapt models using techniques like LoRA and QLoRA when prompt engineering and RAG methods are insufficient.
- Engineer, version, and manage production prompts, integrating prompt and context management into the application layer.
- Integrate LLM APIs from providers such as OpenAI, Anthropic, and Azure OpenAI, as well as open-source model endpoints, ensuring proper authentication, rate limiting, and cost control.
- Instrument applications for evaluation by implementing output logging, quality scoring, and human-feedback loops.
- Optimize latency and token usage through caching, batching, and intelligent model routing strategies.
- Translate client business requirements into detailed GenAI feature specifications.
- Communicate technical trade-offs related to cost, latency, and accuracy to non-technical product stakeholders.
- Collaborate closely with AI architects and data scientists on shared components and solutions.
- Document architecture and prompt design decisions to ensure maintainability and smooth handoffs.

Required Qualifications

- 4 to 8 years of software engineering experience, with 1 to 3 years focused on hands-on GenAI and LLM application development.
- Proficiency in Python and experience with orchestration frameworks such as LangChain or LlamaIndex.
- Familiarity with vector databases and embedding strategies, including Pinecone, Weaviate, and pgvector, as well as knowledge-graph or graph-database tools like Neo4j where applicable.
- Deep understanding of LLM failure modes such as hallucination, context window limitations, and cost blowups, with the ability to design effective mitigations.
- Experience with model fine-tuning techniques like LoRA and QLoRA, along with evaluation harnesses.
- Practical knowledge of enterprise GenAI and agentic platforms such as Microsoft Azure AI Foundry, AWS Bedrock (including Strands Agents SDK), and Google Vertex AI. Experience with open-source frameworks like LangChain and LlamaIndex is a plus.
- Strong API design and integration skills, including handling authentication, rate limiting, and streaming responses.
- Familiarity with prompt versioning and LLMOps tools such as LangSmith or Weights & Biases.
- Clear and concise technical writing skills, capable of documenting complex RAG architectures for non-technical stakeholders.
- Comfortable working directly with client engineers during embedded delivery engagements.
- Collaborative mindset, able to work effectively with architects, data scientists, and QA teams without requiring fully specified instructions.

Preferred Qualifications and Benefits

While not explicitly listed, candidates with experience in open-source GenAI frameworks and those who demonstrate strong communication and teamwork skills will thrive in this role. The position offers the opportunity to work on cutting-edge AI technologies in a collaborative environment, contributing to impactful enterprise solutions that move beyond demos to production deployment.