GenAI Engineer

BDIPlus•New York, NY

2d•$95,000 - $120,000•Onsite

About The Position

As a Generative AI Engineer at BDIPlus, 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 passionate about building real-world GenAI applications and bringing them into production, while continuously improving performance, reliability and user outcomes.

Requirements

Master's degree or equivalent experience required; PhD a plus
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 Haves

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
Finance domain familiarity (risk, compliance, research workflows, client communications)

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 playbooks to enable internal teams post-engagement.