How will you make an impact?

You will play a pivotal role in designing and delivering reliable, robust AI applications, algorithms, and frameworks that elevate the quality and performance of our product offerings. You will collaborate with and learn from a dedicated team of algorithm and software developers, revolutionizing healthcare through low-cost and high efficiency diagnostic systems.

What will you do?

• Architect, build and deploy LLM powered agent systems (chatbots, copilots, agents) that are safe, fast, and cost-efficient.

• Own the whole product development life cycle: build → prototype → evaluate → harden → monitor.

• Build retrieval-augmented generation (RAG) pipelines (indexing, chunking, embeddings, reranking, grounding).

• Apply context engineering (prompt design, tool calling, memory, compression, window strategy).

• Integrate tools through Model Context Protocol (MCP) and other agent frameworks.

• Design and deploy production-grade chatbots with multi-turn conversation flows, escalation mechanisms, and integrated safety guardrails for seamless use across web, mobile, and internal platforms.

• Implement risk controls (safety filters, jailbreak resistance, PII redaction, abuse detection, audit logs).

• Optimize performance (latency, efficiency, token/cost budgets, streaming, caching, model routing).

• Establish evaluation: golden sets, RAG/grounding scores, toxicity, A/B tests, latency & cost benchmarks.

• Operate in production: tracing, prompt/version lineage, drift detection, incident response, SLOs.

• Collaborate with cross-functional teams, including software, hardware, and data science, to ensure algorithms meet product requirements and are well-integrated into production systems.

• Mentor junior AI engineers, set coding standards and documentation, and advocate for guidelines in LLM engineering including reproducibility, ensuring algorithm reliability and transparency.

• Stay informed on new technologies and industry standards to continuously improve development and evaluation methodologies.

How will you get here?

Education

• Master’s degree in Computer Sciences, Mathematics, Statistics, Bioinformatics or a related field; a Ph.D or equivalent experience is highly preferred.

Experience and skills Required

• 5+ years of hands-on experience in production-level chatbots development, including at least 1 year experience in building LLM-based agents.

• Hands-on with major LLMs/APIs(Open AI, LangChain or Anthropic, Hugging Face etc); Expertise in prompt and context engineering for LLMs.

• Deep experience with RAG pipelines and vector/hybrid search (e.g., FAISS, pgvector, Pinecone), rerankers, and grounding/citation techniques.

• Experience developing and integrating tools using Model Context Protocol (MCP), including defining tool capabilities and managing access permissions.

• Demonstrated skills in developing resilient chatbots incorporating state management, tool/function integration, fallback strategies, and multilingual support.

• Proficient programming abilities in Python (mandatory); familiarity with TypeScript, Java/JavaScript, or Matlab is advantageous.

• Experience managing AI agent safety, including content moderation, policy enforcement, red-teaming, and hallucination mitigation.

• Hands-on experience with AI Agent system performance profiling, batching/streaming, async/concurrency, timely caching, cost/latency budgeting.

• Experience evaluating LLMs using tools like RAGAS, G-Eval, or similar; familiarity with offline/online metrics and A/B testing frameworks.

• Experience managing the lifecycle of LLMs in production, including versioning, rollback, and continuous improvement, cloud-based CI/CD and containerized deployments

• Strong communication skills with the ability to present work to both technical specialists and non-experts.

• Ability to work independently and collaboratively in cross-functional teams.

• Dedicated and motivated: capable of defining ambiguous tasks, establishing clear goals, iterating rapidly, requesting feedback, and consistently following through.

Preferred

• Familiarity with data systems: SQL/NoSQL, message queues, object storage, and schema design for documents and metadata.

• Understanding of AI system security, data privacy, and compliance considerations in production environments.

• Proven proficiency in advising junior AI engineers and supporting team-level technical direction.

• Experience with observability tools and practices, including logging, distributed tracing (e.g., OpenTelemetry or equivalent experience), and metrics monitoring (e.g., Prometheus, Grafana).

• Hands-on experience with AWS SageMaker, Bedrock, and Step Functions, along with other relevant AWS services, to build, deploy, and orchestrate AI agents in scalable, production-grade workflows.

• Experience in biotechnology industry is a plus.