Generative AI Engineer

Responsibilities

• Deliver GenAI solutions end-to-end
• Own technical design and implementation of GenAI applications from discovery through production handoff
• Build APIs/services that integrate with enterprise systems and analytics platforms
• Implement enterprise-grade RAG
• Design ingestion pipelines for internal content (PDFs, policies, research, dashboards, ticketing, wikis)
• Build retrieval systems with hybrid search, filtering, re-ranking, query rewriting, and context optimization
• Implement permission-aware retrieval aligned to entitlements and data access policies
• Establish evaluation and quality controls
• Define metrics for retrieval quality and answer grounding (faithfulness, citation accuracy, coverage)
• Create golden datasets, regression tests, and automated evaluation harnesses
• Operationalize GenAI (LLMOps)
• Instrument observability (latency, cost, token usage, error rates) and implement safe rollout patterns
• Implement caching, rate limiting, fallbacks, and incident-ready operational practices
• Partner across teams to land solutions
• Collaborate with business owners to translate requirements into workable designs
• Work with Security/Compliance to embed guardrails, auditability, and privacy controls
• Provide clear documentation and implementation of playbooks to enable internal teams' post-engagement

Requirements

• Mastery of Python and backend engineering skills (FastAPI/Flask), plus strong SQL
• Experience working in regulated or security-conscious environments, with knowledge of: access controls/entitlements, data privacy, logging/audit trails, secure SDLC practices
• Proven ability to work effectively as an IC consultant: communicate architecture decisions clearly, influence cross-functional stakeholders without direct authority, produce high-quality documentation and handoff materials

Qualifications

• Master's degree or equivalent experience required
• 3+ years in software engineering, data engineering, ML engineering, or applied AI, including recent GenAI delivery in production
• Demonstrated expertise in RAG system design and optimization, including: chunking + metadata enrichment, hybrid search, re-ranking, retrieval evaluation grounding/citations and hallucination mitigation patterns

Skills

• Fine-tuning experience (SFT, LoRA/QLoRA) and familiarity with preference optimization concepts (DPO/RLHF)
• Vector/hybrid search platforms: Elasticsearch/OpenSearch vector, FAISS, Pinecone, Weaviate, Milvus
• LLMOps tooling: MLflow/W&B, OpenTelemetry, prompt registries, evaluation frameworks
• Cloud + platform: AWS/Azure/GCP, Docker/Kubernetes, Terraform
• Tools & Technologies: LLM frameworks (LangChain, LlamaIndex, Semantic Kernel), vector/hybrid search (Open to different skillsets), Data: (Snowflake/Databricks/warehouse), event pipelines, document stores, Observability: logging/tracing/metrics, dashboards, alerting

Benefits

Competitive salary with equity options
Robust health, dental, and vision benefits for employee and dependents
401k matching contributions
Generous PTO policy
Provided work-from-home equipment