Artificial Intelligence Engineer

Exp: 6years + Location: Pan India

About the Role

As an Azure Data Scientist/AI Engineer for Agent Development you will be at the forefront of our AI initiatives transforming complex business problems into sophisticated agent-based solutions. You will work closely with cross-functional teams to understand requirements, design agent architectures, implement and optimize agentic behaviors, and ensure seamless integration and deployment on Azure.

Responsibilities

• Agent System Design Architecture:

  Design, develop and implement end-to-end agent-based AI systems using frameworks such as Autogen, Langgraph, and CrewAI. This includes defining agent roles, communication protocols, and task orchestration.

• Azure Platform Expertise:

  Leverage a wide range of Azure services including Azure Machine Learning, Azure OpenAI Service, Azure Databricks, Azure Functions, and other relevant data and AI services for building, training, deploying, and managing agent solutions.

• Large Language Model (LLM) Integration:

  Integrate and fine-tune LLMs (e.g., GPT-x models) via Azure OpenAI within agentic workflows, focusing on prompt engineering, context management, and optimizing LLM interactions for specific tasks.

• Data Preparation & Feature Engineering:

  Work with diverse datasets on Azure, performing data cleaning, transformation, and feature engineering to prepare data for agent consumption and to enhance agent performance.

• Workflow Orchestration:

  Develop and manage complex stateful agent workflows using Langgraph for intricate decision-making processes, loops, and conditional logic.

• Multi-Agent Collaboration:

  Implement and optimize multi-agent collaboration strategies using Autogen to enable agents to converse, delegate tasks, and collectively solve problems.

• CrewAI Implementation:

  Utilize CrewAI to define and orchestrate collaborative agent crews with distinct roles, tools, and shared objectives for streamlined task execution.

• Tool Development:

  Develop custom tools and integrations for agents to interact with external APIs, databases, and other systems, extending their capabilities.

• Deployment (MLOps):

  Operationalize agent solutions on Azure including containerization (Docker, Kubernetes/AKS), setting up CI/CD pipelines, monitoring agent performance, and implementing robust MLOps practices for continuous improvement.

• Performance Optimization & Scalability:

  Identify and implement strategies to optimize the performance, efficiency, and scalability of agent systems on Azure.

• Research & Innovation:

  Stay up-to-date with the latest advancements in AI, LLMs, and agentic AI frameworks, researching and evaluating new technologies for potential application.

• Collaboration & Communication:

  Work effectively with data engineers, software engineers, product managers, and business stakeholders to translate requirements into technical solutions and communicate complex concepts clearly.

• Responsible AI:

  Ensure the ethical development and deployment of AI agents, addressing bias, fairness, transparency, and security concerns.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field. If PhD, will be good.
• Proven experience (1-2yrs) as a Data Scientist or AI Engineer with a strong focus on building and deploying machine learning or AI solutions.
• Deep expertise in Azure cloud services, specifically Azure Machine Learning, Azure OpenAI Service, Azure Databricks, Azure Functions, Azure Data Lake Storage, etc.

Required Skills

• Strong programming skills in Python, Python, PyTorch, TensorFlow, Scikit-learn.
• Hands-on experience with agent orchestration frameworks:
• Autogen: Experience in building multi-agent conversational systems and enabling dynamic collaboration.
• Langgraph: Proficiency in designing and implementing stateful graph-based agent workflows with complex logic.
• CrewAI: Experience in orchestrating role-playing autonomous AI agents for collaborative task execution.
• Solid understanding of Large Language Models (LLMs), prompt engineering, and RAG (Retrieval Augmented Generation) techniques.
• Experience with MLOps practices including CI/CD, model monitoring, and version control (Git).
• Familiarity with containerization technologies (Docker, Kubernetes).
• Strong analytical and problem-solving skills with the ability to translate business requirements into technical solutions.
• Excellent communication and collaboration skills.

Preferred Skills

• Experience with additional programming languages or frameworks.
• Knowledge of industry-specific applications of AI.

Equal Opportunity Statement

We are committed to diversity and inclusivity in our hiring practices.