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Innovaccer is seeking an AI Engineering Lead to own a defined slice of their Applied AI roadmap, architecting agentic and retrieval systems while growing a small team of engineers. This is a hands-on leadership role where you will continue writing and reviewing code while owning delivery, quality, and team growth.
You will own AI systems end-to-end, from problem framing with product and customer teams through architecture, evaluation, deployment, and cost/latency optimization. Key responsibilities include:
**System Ownership:**
- Design production agentic and RAG systems meeting real customer scale and reliability requirements
- Apply agent design patterns with judgment (memory, tool routing, multi-agent coordination, explicit failure handling)
- Own the retrieval pipeline as a first-class system: chunking strategy, embedding model selection, vector stores, re-ranking, and relevance tuning
- Manage LLMOps lifecycle: model and prompt versioning, eval pipelines in CI, observability (tracing, token and cost dashboards), guardrails and safety filters
- Select, fine-tune, and serve SLMs and open models using LoRA/QLoRA, quantization, and inference optimization
- Optimize latency, cost, and accuracy together, making defensible build-versus-buy and model-selection decisions
**Beyond the Codebase:**
- Define and execute quarterly roadmap for your area, translating ambiguous business problems into clear ML solution workflows
- Work directly with business leaders and customers to understand workflow breakpoints and build solutions
- Partner with data platform and applications teams to integrate capabilities into products
- Set coding and evaluation standards for your team and lead design reviews
- Pursue published work or patents in healthcare AI where warranted
Innovaccer's Analytics team builds production ML systems that turn fragmented healthcare data into actionable decisions: risk scores flagging deteriorating patients, operational models surfacing inefficiencies, and prescriptive tools guiding next steps. Analytics is core product infrastructure.
**Requirements:**
- 6+ years in data science, applied ML, or AI engineering, including 2+ years building LLM-powered products (healthcare experience a plus)
- Deep NLP and GenAI experience; statistical and classical ML a plus
- Strong hands-on Python for highly scalable, performant enterprise applications with optimization techniques
- Hands-on experience with PyTorch and/or HuggingFace transformers
- At least one shipped GenAI product with genuinely complex architecture (multiple agents, memory, retrieval, agent OTEL/tracing in production)
- Working command of modern fine-tuning: PEFT methods, LoRA and QLoRA preferred
- Hands-on experience with at least one ML platform (Databricks, Azure ML, or SageMaker)
- Experience leading engineers formally or as technical lead; owned other people's output
- Strong written and spoken communication with customer-focused instinct
- Preferably Master's in Computer Science, Computer Engineering, or related field
**Engineering Baseline (assumed independent delivery capability):**
- Build production-grade RAG and LLM/SLM-powered features end-to-end with limited supervision
- Work fluently in orchestration frameworks (LangGraph, LlamaIndex, CrewAI, or equivalent)
- Design and tune retrieval pipelines for relevance
- Implement prompt engineering, function/tool calling, reliable structured-output parsing
- Write and run evals (golden sets, LLM-as-judge) to measure quality and catch regressions
- Containerize and deploy services (Docker, REST/gRPC) with attention to latency, token cost, basic guardrails
- Document well and participate actively in code review
**Good to Have:**
- API frameworks for robust web applications (FastAPI or Django preferred)
- Comfort with at least one hyperscaler cloud
- Papers or patents, especially in healthcare AI
