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). This role is for an engineer who can close that gap in practice, not just on paper, with solid foundations in machine learning, LLMs, data engineering, and cloud platforms, and genuine hands-on ability to productionize models at scale for a confidential enterprise client.

What You Will Do

- 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
- Conduct proofs of concept, demos, and conceptual solutioning

What You Need to Succeed
- 3-5 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)
- 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
- Experience building RAG architectures end to end, from chunking and retrieval strategy through to generation quality
- Comfort working within enterprise security, compliance, and ethical AI guardrails, not just a research or sandbox environment
- Experience presenting AI capabilities and trade-offs to non-technical business stakeholders, not only to other engineers

What Qfyre Offers
- Hands-on AI engineering ownership on a live enterprise build-out, not a prototype or internal tooling project
- Direct exposure to production-grade LLM and RAG systems at enterprise scale
- 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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📌 AI Engineer, GenAI and MLOps (Bangalore Metropolitan Area)
🏢 Qfyre Techlabs
📍 Bangalore Metropolitan Area