Job Description
As 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.
Qualifications
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.
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 Information
What We Offer
Afficiency is an Equal Opportunity Employer. All your information will be kept confidential according to EEO guidelines.