Description
Summary:
AI/ML Engineer with 3+ years experience in developing and operationalizing scalable Machine Learning models and designing/deploying AI Agent workflows.
Highlights:
1. Developing state-of-the-art and scalable Machine Learning models
2. Designing and deploying AI Agent workflows and pipelines
3. Hands-on experience in Agent Ops and LLM lifecycle management
**Job Summary: \-**
AI/ML Engineer with good hands\-on experience of 3\+ years in developing state of the art and scalable Machine Learning models and their operationalization, as well as designing and deploying AI Agent workflows and pipelines, leveraging off\-the\-shelf workbench production.
**Job Responsibilities: \-**
* Hands on experience in Python data\-science and math packages such as NumPy, Pandas, Sklearn, Seaborn, PyCaret, Matplotlib
* Proficiency in Python and common Machine Learning frameworks (TensorFlow, NLTK, Stanford NLP, PyTorch, Ling Pipe, Caffe, Keras, SparkML and OpenAI etc.)
* Experience of working in large teams and using collaboration tools like GIT, Jira and Confluence
* Good understanding of any of the cloud platform – AWS, Azure or GCP
* Understanding of Commercial Pharma landscape and Patient Data / Analytics would be a huge plus
* Should have an attitude of willingness to learn, accepting the challenging environment and confidence in delivering the results within timelines. Should be inclined towards self motivation and self\-driven to find solutions for problems.
* **Agent Ops:** Hands\-on experience in designing, deploying, and monitoring AI agent workflows using any frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or equivalent agentic orchestration tools
* Experience with prompt engineering, tool/function calling, RAG (Retrieval\-Augmented Generation) pipelines, and memory/context management for multi\-step agentic systems
* Familiarity with LLM lifecycle management including fine\-tuning, evaluation, guardrails, and deployment of foundation models via APIs (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or equivalent)
Experience operationalizing agent\-based systems including observability, tracing, latency optimization, cost monitoring, and failure handling for production\-grade deployments