Meaningful work,
meaningful life

KEY ACCOUNTABILITIES:

• Design, develop, and maintain AI/ML and open-source LLM-based solutions (e.g., NLP, RAG, document intelligence, forecasting, automation).

• Deploy AI solutions using self-hosted, on-premise, or private cloud infrastructure, ensuring full data ownership and security.

• Strictly ensure that no external AI APIs (e.g., ChatGPT, Gemini, Claude, Copilot, or similar SaaS-based AI services) are used in any solution.

• Provide operational support and troubleshooting for AI systems, ensuring reliability, accuracy, and performance in production.

• Work closely with data engineering, platform, and application teams to ensure seamless integration of AI components.

• Implement monitoring for model performance, data drift, latency, and system health using enterprise observability tools.

• Ensure compliance with internal security policies, data privacy regulations, and Responsible AI guidelines.

• Continuously improve AI pipelines and models through retraining, evaluation, and optimization.

• Provide guidance and best practices to development and change management teams for AI initiatives.

• Maintain clear documentation and communication with stakeholders on AI limitations, risks, and outcomes.

• Headquarter AI governance and architecture teams

SKILLS AND ATTRIBUTES:   

• Strong experience in designing and deploying open-source AI/ML solutions in enterprise environments

• Hands-on expertise with open-source LLMs (e.g., LLaMA, Mistral, Qwen, Falcon, Gemma) and related ecosystems

• Proficiency in Python and AI/ML frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn)

• Experience building RAG pipelines, vector search, embeddings, and document intelligence systems using open-source components

• Strong understanding of MLOps for self-hosted models (CI/CD, model versioning, monitoring, retraining)

• Experience integrating AI services via internal APIs and microservices

• Excellent analytical, debugging, and problem-solving skills

• Strong communication skills to explain AI systems to non-technical stakeholders

• Cross-functional mindset with a strong sense of ownership and accountability

• 2-4 years of experience in AI/ML engineering or applied data science roles

• Proven experience deploying open-source models in production without dependency on external AI APIs

 Advantage -

• Experience working in large, multi-country organizations with strict data governance

• Hands-on experience with on-premise GPU environments or private cloud AI stacks

• Experience with GenAI frameworks (LangChain, LlamaIndex) using local or self-hosted inference only

• Experience with vector databases (FAISS, Milvus, Weaviate)

• Strong SQL knowledge and data pipeline experience

• Experience with Docker, Kubernetes, Rancher

• Familiarity with observability tools (Prometheus, Grafana, Kibana)

• Experience with JIRA, Confluence, Bitbucket, Git

• Knowledge of Responsible AI, model risk management, and auditability

• Frontend or backend development experience for AI-enabled applications

EDUCATION:

• Educated to degree level or equivalent in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline