Senior AI Engineer, GenAI and MLOps

About This Role
India has roughly one qualified AI engineer for every ten open GenAI roles (TeamLease Digital), and NASSCOM projects a shortfall of close to one million AI-skilled professionals by 2027 (NASSCOM and Deloitte). At the senior end of that gap, the scarcity is sharper still: engineers who have actually carried an AI or GenAI system through to production, not just a notebook or a demo. This role is for someone who can take ownership of that system end to end, with strong foundations in machine learning, LLMs, data engineering, and cloud platforms, for a confidential enterprise client.
What You Will Own
Design, build, and deploy machine learning and generative AI models, including LLMs, embeddings, transformers, and RAG pipelines
Develop scalable AI services and microservices using Python, REST APIs, and cloud-native technologies, optimised for performance, accuracy, and cost efficiency
Work with structured and unstructured datasets for feature engineering, vectorisation, and model training, and build data pipelines for training, validation, and inference
Collaborate with data engineering teams on data ingestion, storage, and governance
Implement CI/CD pipelines for ML models (MLOps), monitor model performance and drift, and implement retraining strategies
Manage model lifecycle, logging, and observability
Integrate AI systems with enterprise applications, APIs, and cloud platforms (Azure, AWS, or GCP)
Build Retrieval-Augmented Generation (RAG) architectures using vector databases such as Pinecone, FAISS, Weaviate, or Azure AI Search, aligned with enterprise security, compliance, and ethical AI standards
Work with product, engineering, domain experts, and business teams to translate requirements into technical solutions, and communicate AI capabilities and limitations to non-technical stakeholders
Lead proofs of concept, demos, and conceptual solutioning, and bring junior engineers up to speed on production AI practices
What You Need to Succeed
5-8 years of relevant industry experience, with strong proficiency in Python (NumPy, Pandas, PyTorch, TensorFlow, Transformers)
Hands-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, Llama, or equivalent), including systems that have reached production, not only pilots
Expertise in ML algorithms, NLP, deep learning, and vector embeddings
Experience with cloud platforms (Azure, AWS, or GCP) and serverless compute
Familiarity with MLOps tools such as MLflow, Kubeflow, Azure ML, SageMaker, or Databricks
Experience using vector databases such as Pinecone, Chroma, FAISS, or Azure AI Search
Knowledge of containerisation with Docker and Kubernetes
What Will Give You an Edge
Having taken architectural ownership of a RAG or LLM system from design through to production incident response
Experience operating within enterprise security, compliance, and ethical AI guardrails at scale, not only in a research or sandbox environment
A track record of translating AI capabilities and trade-offs for non-technical business stakeholders and senior leadership
What Qfyre Offers
Senior-level ownership on a live enterprise AI build-out, with real architectural decision-making, not a prototyping sandbox
Choice of hiring city across Bengaluru, Chennai, Pune, or Hyderabad
A confidential search process managed by a specialist talent partner, not a high-volume vendor
Skills and Technologies
PythonPyTorchLLMsRAGMLOpsVector DBAzure/AWS/GCPKubernetes
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