Purpose of the Role
This role will be responsible for designing AI & Machine Learning solutions on cloud-based platforms, explore emerging trends in AI, develop proof-of-concepts and engage with internal and external ecosystem to progress the proof of concepts to production.
Key Roles & Responsibilities:
- This role focuses on consuming, integrating, and operationalizing advanced AI models—from Large Language Models (LLMs) to Small Language Models (SLMs) - into secure, governed, and scalable business solutions.
- Governance by Design: enforce data-handling policies in code (prompt redaction middleware, retrieval allow-lists, per-use-case policies).
- Prompt/Agent CI/CD: add evaluation gates (answer quality, safety) to pipelines; canary deploys with feature flags; automated rollback on drift.
- Model Lifecycle: manage SLM/LoRA fine-tuning with consented datasets, synthetic augmentation policies, and model registry entries w/ lineage.
- Observability: implement tracing (e.g., request → retrieved docs → model output → tool calls), latency & cost SLOs; alerts on hallucination/safety incidents.
- Provider Abstraction: wrap OpenAI/Gemini/Azure OpenAI/Vertex behind an interface; capture provider/region, model/version, and quota routing.
Core AI/ML Skills:
- Proficient in Python, PyTorch, TensorFlow, or similar frameworks
- Experience with supervised, unsupervised, and reinforcement learning
- NLP Expertise: Solid grounding in Natural Language Processing (NLP) concepts — tokenization, embeddings, semantic search, text classification, and summarization.
- Generative AI & LLMs: Strong understanding of Large Language Models (LLMs) and Generative AI (GAI), with hands-on experience in LangGraph, LangChain, LlamaIndex, OpenAI APIs and Model Context Protocol (MCP) for building AI agents and conversational systems, Transformers, and Prompt engineering
- Strong understanding of statistics, probability, and model evaluation techniques
Hands on Java, Cloud and Kubernetes principles,
- Must have experience in architecting & development of applications on cloud infrastructure using different AWS services (or other public cloud like IBM Cloud, Azure, Google Cloud).
- Experience with AWS Cloud paradigms like lambda, cloudfront, s3
- Experienced with different forms of architectures like SOA, Microservices, EDA ….
- Responsive Web applications, Web Services and batch applications development
- Application development tools and frameworks like maven, ant, check style, PMD, fortify, junit, SONAR, SOAP UI, REST Assured etc.
- Hands on with Java/J2EE design patterns
Must-Have Skills
- Define and enforce AI data-handling policies (PII/PCI/GDPR) across prompts, retrieval, logs, and analytics. Implement redaction/masking, tenant isolation, model risk tiers, and provider due diligence. Own evaluation and approval workflows for prompt/model changes, with audit-ready lineage and retention controls.
- API Consumers: integrate ChatGPT (OpenAI) and Gemini via provider SDKs with fallback logic and request/response schemas.
Good-to-Have Skills
- Broader understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning).
- Exposure to multimodal AI (text + vision, speech).
- Data engineering & analysis skills: ETL pipelines, feature engineering, EDA.
- Familiarity with MLOps/DevOps (CI/CD pipelines, monitoring, retraining).
- Understanding of knowledge graphs, embeddings optimization, and enterprise search integration.
- Strong collaboration and communication skills to work with cross-functional teams and explain AI concepts to stakeholders.
