Broad Function:
We are looking for a Senior AI/ML Engineer to join our AI Platform team and drive the design, development, and production deployment of intelligent agentic systems. You will work at the intersection of cutting-edge Gen AI research and real-world engineering, owning end-to-end delivery of LLM-powered products — from RAG pipelines and multi-agent orchestration to model fine-tuning, safety guardrails, and observability.
This is a hands-on, high-ownership role. You will mentor engineers, influence architecture decisions, and collaborate closely with product and data science teams to ship AI features that are reliable, scalable, and safe
Purpose of the Role:
To design, build, and deploy scalable AI/ML solutions, including LLM-powered and agentic systems, ensuring high performance, reliability, and safety. Drive end-to-end delivery of AI products while mentoring teams and influencing architecture to enable impactful, production-ready AI capabilities.
Roles and Responsibilities (not limited to):
1. Core AI & Generative AI:
Design and develop advanced AI systems including:
- Agentic RAG, Adaptive RAG pipelines
- Multi-Agent Systems (MAS)
- Evaluate and optimize LLM outputs based on:
- Retrieval quality
- Answer relevance and faithfulness
- Build scalable AI workflows using frameworks such as Lang Chain and Lang Graph Testing and Support.
2. Model Fine-Tuning & Alignment:
Implement fine-tuning techniques such as:
- Enhance model performance through alignment strategies ensuring:
- Accuracy
- Safety
- Domain relevance
3. AI Protocols & Integration:
Work with emerging AI standards including:
- Model Context Protocol (MCP)
- Agent-to-Agent (A2A) communication
- Integrate AI systems into enterprise environments
- Leverage tools such as Agent Development Kit (ADK)
4. Machine Learning & NLP:
Apply strong ML fundamentals including:
- Model evaluation, cross-validation, and hyperparameter tuning
- Work with NLP concepts such as:
- Tokenization, embeddings, transformers, attention mechanisms
- Develop models using PyTorch and/or TensorFlow
5. Engineering & Production:
- Develop scalable, production-grade applications using Python
- Optimize databases using SQL/PostgreSQL
- Work on full-stack integrations
6. Data & Vector Infrastructure:
Design and implement vector-based architecture using pgvector, Pinecone, Weaviate, Qdrant, etc.- Build: Embedding pipelines
- Chunking strategies
- Scalable similarity search systems
7. MLOps & Observability:
Manage ML lifecycle using tools like: MLflow, Weights & Biases- Implement monitoring for:
- Model drift
- Accuracy degradation
- Data quality issues
8. Prompt Engineering & LLM Safety:
Design advanced prompting strategies: Chain of Thought (CoT), few-shot, prompt tuning- Mitigate risks such as:
- Prompt injection
- Adversarial inputs
9. AI Safety & Guardrails:
Implement guardrails to ensure safe and compliant AI outputs:
- Content filtering
- Toxicity detection
- Response boundary control
- Collaborate with security and compliance teams to ensure adherence to policies and regulations
CULTURAL TRAITS (Critical Fit Requirements):
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High ownership with end-to-end delivery mindset in a fast-paced environment
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Strong focus on innovation and continuous learning in AI/GenAI
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Effective cross-functional collaboration across teams
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Comfortable in high-impact, stakeholder-facing roles
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Agile problem-solver with strong accountability
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Committed to secure, ethical, and responsible AI development
Desired Qualifications & Experience:
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6–8 years of overall experience in software development.
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3–4 years of hands-on experience in AI/ML roles, preferably within a product-based organization (non-services/consulting).
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Proven track record of building and deploying AI/ML solutions from prototypes to production.
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Demonstrated experience in leading and mentoring teams of 5+ engineers, including conducting code reviews, design reviews, and providing career guidance.
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Strong cross-functional collaboration skills, with the ability to effectively work with product managers, data scientists, and platform/engineering teams.
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Open-source contributions to AI/ML frameworks.
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Experience with Graph RAG or Knowledge Graph-backed retrieval.
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Familiarity with ADK (Agent Development Kit) or similar low-level agent runtimes.
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Java or React exposure for full-stack AI product integration.
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Proficient in AWS and Azure for deploying and scaling AI/ML workloads.
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Familiarity with managed ML services: SageMaker, Azure ML, Bedrock, or OpenAI API platforms.
The company offers a range of employee benefits including:
- Cashless medical insurance for employees, spouses, and children
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Accidental insurance coverage
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Life insurance coverage
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Retirement benefits including Provident Fund (PF) and Gratuity
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ESI*
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Complementary meal coupons
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Company-paid transportation
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Sodexo benefits for income tax savings
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Paternity & Maternity Leave Benefit
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National Pension Saving
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EL encashment
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Sick Leave
