Job Specifications
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 (eg, 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.