Develop and maintain AI solutions using both Machine Learning and Generative AI approaches, aligned with business and client needs.
Build and improve LLM-based applications, including prompt engineering, orchestration flows, and tool usage.
Implement and optimize RAG pipelines , including chunking, embeddings, ranking, and retrieval strategies.
Work with vector databases such as pgvector, FAISS, Pinecone, or Weaviate to support semantic search and context-aware responses.
Develop integrations using MCP or similar approaches to connect AI systems with external tools, APIs, and enterprise platforms.
Train, evaluate, and deploy ML models for prediction, classification, clustering, or other data-driven use cases.
Contribute to API development and integration of AI capabilities into existing applications and workflows.
Support deployment, monitoring, and optimization of AI services in cloud and/or on-premise environments.
Implement basic guardrails, validation, and monitoring mechanisms to improve quality, reliability, and safe usage of AI systems.
Deploy, configure, and support AI workloads in Azure, AWS, or GCP , using cloud services for compute, storage, networking, security, and monitoring.
Collaborate with cross-functional teams including software engineers, data scientists, and business stakeholders to deliver production-ready solutions.
Stay up to date with relevant developments in AI, ML, and GenAI, and contribute to knowledge sharing within the team.

2
4 years of experience in Python development, including libraries such as Pandas, NumPy, and Scikit-learn.
Hands-on experience building and deploying Machine Learning models in real-world use cases.
Practical experience with LLM-based applications, including prompt engineering and LLM integration.
Experience with frameworks such as LangChain or similar orchestration tools.
Experience designing or implementing RAG pipelines, including document chunking, embeddings, and retrieval strategies.
Hands-on experience with vector databases such as pgvector, FAISS, Pinecone, or Weaviate.
Good understanding of embeddings, semantic search, and information retrieval concepts.
Experience with MCP or similar tool-integration approaches for AI systems.
Experience building and consuming APIs for system integration.
Hands-on experience deploying and operating AI/ML services in at least one major cloud platform: Azure, AWS, or GCP.
Working knowledge of cloud platforms and/or on-premise deployment models.
Understanding of logging, monitoring, and observability fundamentals.
Good communication skills and ability to work effectively in cross-functional teams.

Physical Wellbeing : Our wellbeing program includes medical benefits, gym support, and personalised fitness options for an active lifestyle, complemented by team events and the Healthy Habits Club.
Work-Life Fusion: In very dynamic industries such as IT, the line between our professional and personal lives can quickly become blurred. Having a one-size-fits-one approach gives us the flexibility to define the work-life dynamic that works for us.
Emotional Wellbeing: We believe that to maintain our overall health, we need to invest in our mental wellbeing just as much as we do in our physical health, social connections or in achieving work-life balance.
Social Wellbeing: As a growing community in a hybrid environment, we want to ensure we remain connected not just by the great work we do every day but through our passions and interests.

AI Engineer

Key skills