AI Engineer – Data science & Gen AI

At Innover, we endeavor to see our clients become connected, insight-driven businesses. Our integrated Digital Experiences, Data & Insights and Digital Operations studios help clients embrace digital transformation and drive unique outstanding experiences that apply to the entire customer lifecycle. Our connected studios work in tandem to reimagine the convergence of innovation, technology, people, and business agility to deliver impressive returns on investments. We help organizations capitalize on current trends and game-changing technologies molding them into future-ready enterprises.

Take a look at how each of our studios represents deep pockets of expertise and delivers on the promise of data-driven, connected enterprises.

About the Role

We are looking for a highly motivated AI Engineer with strong foundations in Data Science and hands-on experience in Generative AI and Agentic AI systems. This role is ideal for someone who enjoys both building intelligent models and engineering robust, scalable solutions for production. You will be instrumental in building next-generation AI applications leveraging LLMs, multi-agent workflows, and orchestration frameworks to automate complex tasks across domains like document understanding, workflow intelligence, and customer support.

Key Responsibilities

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  Design and build GenAI-powered applications using LLMs (e.g., GPT, Claude, LLaMA, Mistral) for use cases like summarization, Q&A, document parsing, and chatbots.
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  Build and orchestrate agentic workflows using frameworks like LangChain, LangGraph, Semantic Kernel, autogen etc.
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  Implement retrieval-augmented generation (RAG) pipelines using vector stores and embedding models.
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  Translate business problems into AI workflows involving multi-agent planning, decision-making, and task execution.
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  Collaborating closely with clients to prototype, build, test, and deploy products powered by GenAI/LLM technology on a large scale.
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  Deploy and maintain production-grade AI solutions using MLOps practices, including model monitoring, versioning, and CI/CD pipelines.
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  Develop scalable, modular APIs and microservices around AI capabilities.
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  Collaborate closely with data scientists, ML engineers, and product teams to iterate and refine models.

Must-Have Skills & Experience

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  2 + years of experience in Data Science, Applied AI, or AI Engineering roles.
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  Hands-on experience working with LLMs and GenAI models via APIs or fine-tuning (OpenAI, Azure OpenAI, Hugging Face, etc.).
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  Experience building and deploying agent-based AI systems using tools like LangGraph, LangChain Agents, or Semantic Kernel Planners.
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  Strong proficiency in Python and experience with common AI/ML frameworks (PyTorch, transformers, etc.).
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  2+ years of experience with AWS, GCP or Microsoft Azure,
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  Experience building microservices with Azure Functions and/or AWS Lambda functions
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  1+ years experience with MLOps, CI/CD pipeline development, containerization, model deployment in test and production environments
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  Experience in AI/ML technologies and software engineering,
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  At least 2+ years of hands-on experience with Python and 1+ years of experience building and maintaining scalable API solution

Nice to have Skills

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  Familiarity with multi-agent systems, workflow planners, or autonomous agents.
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  Exposure to LangGraph node/edge orchestration, OpenAI function calling, or tools/plugins.
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  Experience with structured information extraction from unstructured documents (e.g., invoices, forms).
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  Understanding of AI safety, guardrails, or trustworthy AI principles.