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Senior AI Engineer

GE HEALTHCARE -
Bengaluru, Karnataka

Apply Now

Job details

4 days ago

Qualifications

BCS
CI/CD
Azure
Law
Computer Science
Software Engineering
MCP
Kubernetes
Software deployment
Java
Master's degree
Databases
Microservices
OS Kernels
Bachelor's degree
Machine learning
Distributed systems
Neo4j
Continuous integration
Deep learning
Computer Engineering
Natural language processing
APIs
Agile
Data science
Metadata
Medical imaging
AI
TypeScript
Communication skills
Graph databases
Python
Analytics

Full job description

Job Description Summary

We are seeking a highly skilled and innovative Senior Software Engineer – Agentic AI Engineer to join AIS Digital – Imaging360. The role will focus on designing, developing, and deploying production-grade AI solutions that leverage traditional AI/ML, Generative AI, Retrieval-Augmented Generation, knowledge graphs, and agentic workflows to solve complex healthcare imaging problems. The ideal candidate will bring 10+ years of software engineering experience, strong AI engineering depth, and a platform-agnostic mindset with the ability to evaluate and implement solutions across cloud, open-source, and enterprise AI ecosystems.

Job Description

Roles and Responsibilities

In this role, you will:

Design, build, and deploy agentic AI systems that support autonomous reasoning, planning, tool use, multi-step execution, and human-in-the-loop workflows for Imaging360 use cases.
Develop scalable AI/ML and GenAI solutions across the full lifecycle, including data ingestion, feature engineering, model experimentation, evaluation, deployment, monitoring, and continuous improvement.
Architect and implement Retrieval-Augmented Generation solutions that combine structured and unstructured healthcare, product, operational, and workflow data with strong grounding, relevance, and traceability.
Design knowledge graph and GraphRAG solutions that model relationships across imaging assets, clinical entities, devices, and workflows, combining graph traversal with vector retrieval for multi-hop reasoning and explainable grounding.
Engineer evaluation and testing harnesses for agentic systems, including automated eval pipelines, golden datasets, LLM-as-judge scoring, regression suites for prompts and agents, simulation-based agent testing, and red-teaming for adversarial robustness.
Optimize token usage, latency, and inference cost through context engineering, prompt compression, prompt and semantic caching, model routing between frontier and small language models, batching, and structured output constraints.
Build platform-agnostic AI services using appropriate cloud-native, open-source, or enterprise AI capabilities while avoiding dependency on a single AI vendor or model provider.
Integrate LLMs, embedding models, orchestration frameworks, vector stores, APIs, and backend services into secure, reliable, and maintainable production applications.
Create rapid proofs of concept for emerging AI, GenAI, and agentic patterns; harden successful prototypes into reusable production components and engineering patterns.
Collaborate with product managers, architects, UX, data engineering, cybersecurity, quality, regulatory, cloud operations, and scrum teams to translate Imaging360 business needs into responsible AI solutions.
Define and implement AI evaluation methods for accuracy, relevance, grounding, robustness, latency, cost, safety, fairness, explainability, and operational reliability.
Drive engineering excellence through clean architecture, API design, automated testing, CI/CD, documentation, code reviews, design reviews, and production readiness practices.
Ensure AI solutions meet healthcare-grade expectations for privacy, security, auditability, data governance, compliance, and responsible AI adoption.

Technical Skill Set

Programming and software engineering: Strong hands-on experience with Python and modern backend engineering; exposure to Java, TypeScript, or similar languages is preferred.
AI/ML and GenAI: Experience with machine learning, deep learning, NLP, LLM application development, embeddings, prompt engineering, fine-tuning or parameter-efficient tuning, and model evaluation.
Agentic AI: Experience designing AI agents, multi-agent workflows, tool orchestration, reasoning/planning loops, function calling, agent memory, guardrails, and workflow automation.
RAG and knowledge systems: Experience building retrieval pipelines, indexing strategies, chunking, reranking, metadata filtering, vector databases, hybrid search, and grounded response generation.
Knowledge graphs: Experience with graph databases (Neo4j, Amazon Neptune, or equivalent), ontology and entity modeling, entity resolution, GraphRAG patterns, and hybrid graph plus vector retrieval.
Agent evaluation and harness engineering: Experience with evaluation frameworks (Ragas, DeepEval, promptfoo, LangSmith, Langfuse, or equivalent), agent trajectory evaluation, offline and online eval loops, A/B testing, and CI-integrated evaluation gates.
Cost and performance optimization: Token accounting and budgeting, prompt caching, KV-cache-aware design, model right-sizing and routing, quantization and distillation awareness, and cost observability per request and workflow.
Interoperability protocols: Working knowledge of Model Context Protocol (MCP) for tool and data integration and agent-to-agent (A2A) communication patterns; experience building or consuming MCP servers is a plus.
Multimodal AI: Experience with vision-language models for medical imaging context, document understanding (reports, scanned forms), and multimodal RAG.
Architecture and integration: Strong understanding of APIs, microservices, event-driven systems, serverless or containerized architectures, distributed systems, and enterprise integration patterns.
Cloud and MLOps: Experience deploying AI services using cloud-native, container, or Kubernetes-based environments with CI/CD, model serving, observability, monitoring, and cost optimization.
Responsible AI and security: Knowledge of data privacy, secure AI patterns, access controls, content safety, hallucination mitigation, audit logging, governance, and healthcare compliance needs.
Platform-agnostic tooling: Working knowledge of one or more AI platforms or frameworks such as Azure OpenAI, Amazon Bedrock, Google Vertex AI, open-source LLMs, LangChain, LangGraph, LlamaIndex, Semantic Kernel, MLflow, or equivalent technologies.

Education Qualification

Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, Data Science, Biomedical Engineering, or a related STEM discipline with 10+ years of relevant software engineering experience. A master’s degree or equivalent advanced experience in AI/ML, distributed systems, or healthcare technology is preferred.

Desired Characteristics

Experience delivering AI capabilities in regulated healthcare, medical imaging, clinical workflow, DICOM, or enterprise healthcare software environments.
Experience with healthcare interoperability standards (FHIR, HL7, DICOMweb) for grounding AI agents in clinical and operational data.
Ability to translate ambiguous product or workflow needs into AI use cases, technical designs, evaluation plans, and production deployment roadmaps.
Strong understanding of Imaging360-style enterprise platforms that connect cloud services, data pipelines, integrations, customer workflows, and operational analytics.
Hands-on experience with model experimentation, prompt evaluation, RAG quality measurement, agent workflow testing, and production observability dashboards.
Familiarity with AgentOps/LLMOps observability, including tracing agent steps, tool-call telemetry, drift detection, and OpenTelemetry GenAI conventions.
Experience with synthetic data generation and PHI-safe test data for AI evaluation in regulated environments.
Experience with secure and compliant AI solution design, including privacy-by-design, PHI-sensitive workflows, auditability, data minimization, and role-based access control.
Ability to compare AI models, frameworks, and platforms objectively based on business fit, performance, cost, safety, governance, scalability, and maintainability.
Demonstrated technical leadership in mentoring engineers, reviewing designs, establishing reusable patterns, and influencing cross-functional teams without direct authority.
Excellent communication skills with the ability to explain AI architecture, limitations, risks, trade-offs, and outcomes to engineering, product, quality, regulatory, and leadership stakeholders.

Additional Information

Relocation Assistance Provided: Yes

Apply Now

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