Posted on: 07/05/2026
Key Responsibilities :
- Design, develop, and deploy LLM-based and GenAI solutions for real-world use cases
- Build RAG pipelines, LLM workflows, and AI-powered assistants
- Perform prompt engineering, model evaluation, and fine-tuning
- Develop and optimise advanced machine learning models for prediction and analytics
- Work with large-scale structured and unstructured datasets
- Build scalable inference systems and APIs for model deployment
- Integrate AI/ML solutions with existing products and data platforms
- Improve model performance, latency, and cost efficiency
- Collaborate closely with engineering and product teams to productionize AI features
- Monitor, evaluate, and continuously improve deployed models
What Were Looking For :
- Strong proficiency in Python and the ML ecosystem
- Hands-on experience in building LLM and Generative AI applications
- Expertise in prompt engineering, RAG architectures, embeddings, and vector search
- Experience with frameworks such as LangChain, LlamaIndex, or similar tools
- Familiarity with vector databases like FAISS, Milvus, or Pinecone
- Practical experience with PyTorch or TensorFlow
- Experience working with large-scale and distributed datasets
- Proven experience in deploying ML/AI models as APIs or production services
- Strong SQL and data analysis skills
- Understanding of ML lifecycle, evaluation, and monitoring
- Exposure to MLOps practices and experiment tracking tools
- Experience with cloud platforms (AWS/GCP/Azure) is preferred
- Familiarity with streaming or high-volume telemetry data systems is an added advantage
Ideal Candidate Profile :
- Has successfully deployed at least one LLM/GenAI solution in production
- Demonstrates a strong problem-solving and execution mindset
- Comfortable working in fast-paced, ambiguous environments
- Focused on scalable, practical AI solutions rather than just experimentation
- Able to translate business problems into effective AI-driven solutions
- Strong communication and collaboration skills
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