Title: Agentic AI Engineer
Job Summary
We are seeking a highly skilled Agentic AI Engineer / Applied AI Engineer to design, develop, and deploy next-generation AI systems that leverage Large Language Models (LLMs), autonomous agents, retrieval systems, and intelligent workflows to solve complex enterprise problems.
At the Senior Software Engineer, this role requires deep hands-on engineering expertise, strong software architecture skills, and the ability to independently lead the delivery of scalable AI-powered products and platforms. The ideal candidate combines strong software engineering fundamentals with applied AI/ML experience and thrives in fast-paced, innovation-driven environments.
Key Responsibilities
AI & Agentic Systems Development
Design and implement intelligent AI agents capable of reasoning, planning, tool usage, memory management, and multi-step workflow execution.
Build production-grade LLM applications using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar orchestration platforms.
Develop Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and enterprise knowledge systems.
Create autonomous workflows that integrate APIs, tools, databases, and enterprise services.
Applied AI Engineering
Fine-tune, evaluate, and optimize LLMs and generative AI systems for enterprise use cases.
Develop prompt engineering strategies, evaluation pipelines, guardrails, and AI safety mechanisms.
Implement AI observability, monitoring, hallucination detection, and performance optimization solutions.
Work with structured and unstructured data pipelines for AI model consumption.
Software Engineering & Platform Development
Build scalable backend services and APIs using Python, Java, Node.js, or Go.
Design cloud-native AI architectures on platforms such as AWS, Azure, or GCP.
Develop microservices and containerized applications using Docker and Kubernetes.
Implement CI/CD pipelines, testing frameworks, and infrastructure-as-code practices.
Leadership & Collaboration
Lead technical design discussions and architecture reviews.
Mentor junior engineers and provide guidance on AI engineering best practices.
Collaborate with product managers, data scientists, architects, and business stakeholders to deliver AI-driven solutions.
Drive technical innovation and contribute to enterprise AI strategy.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, or related field.
5+ years of software engineering experience.
2+ years of hands-on experience building Generative AI or LLM-based applications.
1+ Claude MCP integrations
Strong programming skills in Python and at least one additional language (Java, Go, JavaScript/TypeScript).
Experience with:
LLM APIs such as OpenAI, Anthropic, or open-source models.
Vector databases such as Pinecone, Weaviate, FAISS, or ChromaDB.
RAG architectures and semantic search.
REST APIs, distributed systems, and scalable backend design.
Experience with cloud platforms:
Amazon Web Services
Microsoft Azure
Google Cloud Platform
Strong understanding of software design patterns, system scalability, and observability.
Preferred Qualifications
Experience with multi-agent orchestration systems.
Familiarity with AI governance, responsible AI, and enterprise security standards.
Experience deploying AI systems in healthcare, retail, insurance, or pharmacy domains.
Knowledge of reinforcement learning, model fine-tuning, or evaluation frameworks.
Contributions to open-source AI projects or published technical research.
Technical Skills
Programming & Frameworks
Python, Java, TypeScript, Go
FastAPI, Flask, Spring Boot, Node.js
LangChain, LangGraph, CrewAI, AutoGen
PyTorch, TensorFlow, Hugging Face
AI & Data Technologies
LLMs, Generative AI, RAG
Vector databases
Prompt engineering
AI evaluation frameworks
NLP and semantic search
Cloud & DevOps
AWS, Azure, GCP (any one)
Docker, Kubernetes
Terraform
GitHub Actions / Jenkins
Monitoring & Observability
Senior-Level Expectations
Independently owns large technical initiatives end-to-end.
Makes architectural decisions with long-term scalability in mind.
Drives engineering excellence and operational maturity.
Influences technical direction across teams.
Balances rapid experimentation with production-grade engineering discipline.
Nice-to-Have Experience
Healthcare or pharmacy technology experience.
Experience building conversational AI platforms or enterprise copilots.
Knowledge of HIPAA, compliance, and regulated AI environments.
Exposure to graph-based orchestration and workflow engines