Job DescriptionAs a Generative AI Engineer at Afficiency, you will be responsible for designing, developing and deploying Generative AI solutions that enhance our core product platforms and client implementations. You will work closely with engineering, data science, and infrastructure teams to build scalable AI-driven applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), model fine-tuning, and reinforcement learning approaches.
This role is ideal for someone who is based in the NYC Metro Area, passionate about building real-world GenAI applications and bringing them into production, while continuously improving performance, reliability, and user outcomes.
QualificationsResponsibilities - 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.
Must Have - Education: 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
- Strong 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
Nice to Have - 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 (optional)
- Vector/hybrid search: Open to different skillsets
- Data: (Snowflake/Databricks/warehouse), event pipelines, document stores
- Observability: logging/tracing/metrics, dashboards, alerting
Additional InformationWhat We Offer - 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
