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Tier-1 brand, mid-level ML role, metro locations, and broad GenAI skillset drive high competition.
GenAI LLM and enterprise MLOps skills are transferable across industries, yielding moderate background sensitivity.
Explicit 3+ years and numerous mandatory GenAI, MLOps, cloud, and LLM tooling requirements increase filtering strictness.
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Design, develop, and productionize scalable ML/GenAI models, focusing on reliability, accuracy, and cost efficiency.
Build Retrieval-Augmented Generation (RAG) pipelines, multi-agent AI frameworks, and deploy transformer-based models for enterprise use cases.
Collaborate with engineering and DevOps teams to scale AI systems, automate model lifecycle processes, and ensure security, compliance, and governance.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field.
Minimum 3+ years of relevant experience in ML/GenAI engineering with production-grade applications.
Strong proficiency in Python 3.x and ML/DL frameworks such as PyTorch, TensorFlow, and Hugging Face.
Experience with LLMs, RAG pipelines, multi-agent systems, cloud-native ML engineering (Azure OpenAI, AWS Bedrock), and container orchestration (Docker/Kubernetes).
Experienced in developing and fine-tuning transformer-based models (GPT, Llama, Mistral) and using advanced GenAI frameworks (LangChain, LlamaIndex, Haystack).
Skilled in architecting multi-agent AI systems including orchestration and reasoning loops with hands-on knowledge of secure GenAI integrations (MCP).
Capable of driving automation in MLOps including CI/CD, monitoring, retraining, and scaling in cloud or hybrid environments.