Senior AI Engineer

Key Responsibilities

1) Agentic AI Architecture & Delivery

Design and implement (multi) agentic workflows where LLMs plan, decompose tasks, invoke tools/APIs, and synthesize answers across heterogeneous data sources and services.

Build retrieval augmented generation (RAG) and hybrid search pipelines to power robust question answering over clinical and operational data.

Design, code, test, document, and maintain high quality, scalable Big Data and cloud solutions.

Develop scalable microservices and APIs for integrating agent capabilities into clinician tools and internal apps.

Create prototypes/POCs and conduct design/code reviews to derisk delivery and raise engineering quality.

2) LLMs, GenAI & Model Adaptation

Leverage and adapt LLMs; perform prompt engineering, grounding, guard railing, and domain adaptation for healthcare terminology and tasks.

Design intelligent frameworks and finetune models for compliance, accuracy, and ethical standards.

Establish evaluation frameworks (automatic + human in the loop) to measure faithfulness, helpfulness, bias, toxicity, privacy leakage, and overall quality.

3) Data & Platform Engineering

Partner with data engineering to build feature/retrieval stores, embeddings pipelines, and ETL/ELT jobs on Spark/Databricks; design analytics models and rules engines.

Define and develop APIs for integrations across the enterprise; improve data access patterns for low latency inference.

4) Delivery, MLOps & Reliability

Own MLOps/LLMOps: CI/CD for models/prompts, automated tests (unit/contract/eval), versioning, lineage, rollback; enable blue/green or canary releases.

Instrument SLOs/SLIs (latency, availability, hallucination/defect rate) and cost KPIs (tokens, GPU hours) with dashboards and alerts.

Lead production deployments on internal platforms (e.g., UAIS) with strong observability, reliability, and cost controls.

5) Security, Privacy & Compliance

Champion HIPAA and regulated industry controls; integrate access controls, PHI/PPI safeguards, data minimization, encryption, and auditability.

Collaborate with legal, compliance, and clinical safety to operationalize Responsible AI principles.

6) Product, Estimation & Collaboration

Analyze and define customer requirements; assist in defining product technical architecture and delivery roadmaps.

Provide effort estimates and inputs for resource planning; collaborate with QA, architecture, and peer teams.

Write technical documentation, support production, and mentor engineers, and keep skills current through continuous learning.