24 Aug
|
StartX Med
|
Hyderabad
24 Aug
StartX Med
Hyderabad
Interactly.ai Hyderabad, Telangana, India
Agentic AI Engineer - Senior
Interactly.ai Hyderabad, Telangana, India
3 days ago 184 applicants
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Sai Swaroop Nannapaneni
Sai Swaroop Nannapaneni
Emerging HR Leader 40 Under 40[ETHR] | Head People and Culture [Meeami Technologies | Interactly.ai]
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're 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
- 3+ years of hands-on Gen AI / LLM engineering experience building and shipping real systems, not just research or prototypes.
- 5+ years of hands-on AI / ML engineering experience overall (Gen AI + pre-Gen AI combined).
- 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.
- Robust 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.
- Seniority level Mid-Senior level
- Employment type Full-time
- Job function Information Technology and Engineering
- Industries Software Development, Artificial Intelligence, and Hospitals and Health Care
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Agentic AI Engineer - Senior
Interactly.ai Hyderabad, Telangana, India
Join or sign in to save this job
Join to save Agentic AI Engineer - Senior at Interactly.ai
Continue with GoogleContinue with Google
Email or phone
Password
Forgot password?
or
Continue with GoogleContinue with Google
New to LinkedIn? Join now
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
Direct message the job poster from Interactly.ai
Sai Swaroop Nannapaneni
Sai Swaroop Nannapaneni
Emerging HR Leader 40 Under 40[ETHR] | Head People and Culture [Meeami Technologies | Interactly.ai]
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
- 3+ years of hands-on Gen AI / LLM engineering experience building and shipping real systems, not just research or prototypes.
- 5+ years of hands-on AI / ML engineering experience overall (Gen AI + pre-Gen AI combined).
- 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.
- Seniority level Mid-Senior level
- Employment type Full-time
- Job function Information Technology and Engineering
- Industries Software Development, Artificial Intelligence, and Hospitals and Health Care
Direct message the job poster from Interactly.ai
Sai Swaroop Nannapaneni
Sai Swaroop Nannapaneni
Emerging HR Leader 40 Under 40[ETHR] | Head People and Culture [Meeami Technologies | Interactly.ai]
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
- 3+ years of hands-on Gen AI / LLM engineering experience building and shipping real systems, not just research or prototypes.
- 5+ years of hands-on AI / ML engineering experience overall (Gen AI + pre-Gen AI combined).
- 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.
- Seniority level Mid-Senior level
- Employment type Full-time
- Job function Information Technology and Engineering
- Industries Software Development, Artificial Intelligence, and Hospitals and Health Care
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
- 3+ years of hands-on Gen AI / LLM engineering experience building and shipping real systems, not just research or prototypes.
- 5+ years of hands-on AI / ML engineering experience overall (Gen AI + pre-Gen AI combined).
- 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.
Referrals increase your chances of interviewing at Interactly.ai by 2x
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📌 Agentic AI Engineer - Senior (Hyderabad)
🏢 StartX Med
📍 Hyderabad