Experience: 5.00 + years
Salary: INR 4000000-5000000 / year (based on experience)
Expected Notice Period: 30 Days
Shift: (GMT+05:30) Asia/Kolkata (IST)
Opportunity Type: Office ()
Placement Type: Full Time Permanent position(Payroll and Compliance to be managed by: Interactly AI)
(*Note: This is a requirement for one of Uplers client - Interactly AI)
What do you need for this opportunity
Must have skills required:
Hipaa compliance, Model finetuning, Distributed Systems, DevOps, Python, FastAPI / Flask, LLM, rag, Model deployment, Workflows
Interactly AI is Looking for:
About Interactly.ai
Interactly.ai is transforming
healthcare operations with AI-powered automation across scheduling, lab follow-ups, eligibility checks, prior authorization, and insurance verification. Our multimodal AI agents work across voice, email, chat, fax, and EHR integrations improving patient access, reducing staff burden, and accelerating care delivery.
We serve
2,000+ physicians and healthcare providers, delivering measurable outcomes, 20–30% fewer no-shows and thousands of staff hours saved. Now, we are scaling our AI platform and we want you to help lead that charge.
Role Overview
As an
Agentic AI Engineer at Interactly.ai,
you'll design, build, and ship LLM-powered, agentic AI systems that run in production at scale. You won't just prototype you'll own pipelines end-to-end, from prompt design and eval frameworks to
distributed microservice deployment and
monitoring. You'll take full ownership of everything you build, including end-to-end testing, so that what ships is production-ready, reliable, and safe from day one.
Key Responsibilities
- Design and build LLM-powered agentic AI pipelines that reason, plan, and execute multi-step workflows with minimal human intervention.
- Own tool & function calling integrations connect LLMs to internal APIs, EHR systems, calendars, and third-party services.
- Build and maintain rigorous eval frameworks: offline eval sets, regression suites, latency benchmarks, and safety/guardrail tests.
- Ship and operate LLMs in production: streaming responses, structured outputs, prompt versioning, cost/latency tracking, and observability.
- Architect and maintain distributed microservices that expose LLM APIs async Python (FastAPI), event-driven pipelines, and real-time WebSocket workflows.
- Build RAG pipelines: chunking strategies, embeddings, vector stores, retrieval tuning, and PHI-safe handling.
- Take full ownership and conduct end-to-end testing for everything you build from unit and integration tests through to production validation and post-deployment monitoring.
- Collaborate closely with product, backend, and QA to define AI requirements and ship reliable, safe features.
Qualifications
- 5+ years of hands-on Gen AI / LLM engineering experience building and shipping real systems, not just research or prototypes.
- Deep LLM expertise in prompt engineering, tool/function calling, structured outputs, chain-of-thought, and agent orchestration (LangChain, LangGraph, or similar).
- Proven eval culture that you treat evals as a first-class concern not an afterthought.
- Production LLM experience, integrations with OpenAI / Azure OpenAI / AWS Bedrock, streaming, cost optimisation, and monitoring.
- Strong software engineering fundamentals in Python (strong), async programming, REST APIs, CI/CD, Docker.
- Distributed systems fluency with microservice design, event-driven architecture (Kafka/SQS/RabbitMQ), and LLM API gateway patterns.
- End-to-end ownership mindset, you write tests, validate in staging, and don't consider a feature done until it's verified in production.
Preferred Skills
Not required for hiring but these make you stand out:
- LLM post-training experience with fine-tuning, RLHF, DPO, or LoRA for domain-adapted or instruction-tuned models.
- Automatic prompt optimisation familiarity with tools or techniques like DSPy, TextGrad, or automated prompt search for systematic prompt improvement.
- Healthcare industry experience, working knowledge of EHR systems, HL7/FHIR standards, HIPAA compliance, or clinical workflows.
- Startup experience where you've worked in a fast-moving, resource constrained environment where you wore multiple hats and shipped quickly.
- Scaling & on-prem deployments experience deploying LLMs at scale, including self-hosted / on-prem model serving (vLLM, TGI, Triton) or hybrid cloud architectures.
Interview Process
- Round 1 - Screening (1 hr), deep dive to verify hands-on experience.
- Round 2 - Assignment (24–48 hrs): Build a multi-agent system for a narrow use case.
- Round 3 - Present the assignment, design choices, and scalability considerations.
- Final Round - Discussion with the CTO/CEO.
How to apply for this opportunity
- Step 1: Click On Apply! And Register or Login on our portal.
- Step 2: Complete the Screening Form & Upload updated Resume
- Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
About Uplers:
Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.
(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).
So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!
