Generative AI Engineer

Job Profile Specification : Senior Generative AI / Agentic AI Engineer (5–6 years)s

Role summary :

Senior-level engineer (5–6 years of professional experience) focused on designing, building, and deploying production-grade generative AI and agentic-AI solutions.

Responsible for delivering secure, scalable, and business-oriented AI systems that operate on structured and unstructured data and enable AI-driven decision-making

Required experience

5–6 years of industry experience in software engineering and AI-related roles.

Minimum 2-3 years of direct experience with Generative AI and Large Language Models (LLMs).

Key Responsibilities :

Architect, develop, test, and deploy generative-AI solutions (online / offline LLMs, SLMs, TLMs) for domain-specific use cases.

Design and implement agentic AI workflows and orchestration using frameworks such as LangGraph, Crew AI, or equivalent.

Integrate enterprise knowledge bases and external data sources via vector databases and Retrieval-Augmented Generation (RAG).

Build and productionize ingestion, preprocessing, indexing, and retrieval pipelines for structured and unstructured data (text, tables, documents, images).

Implement fine-tuning, prompt engineering, evaluation metrics, A / B testing, and iterative model improvement cycles.

Conduct / model red-teaming and vulnerability assessments of LLMs and chat systems using tools like Garak (Generative AI Red-teaming & Assessment Kit).

Collaborate with MLOps / platform teams to containerize, monitor, version, and scale models (CI / CD, model registry, observability).

Ensure model safety, bias mitigation, access controls, and data privacy compliance in deployed solutions.

Translate business requirements into technical designs with clear performance, cost, and safety constraints.

Required Skills and Experience :

Strong proficiency in Python and experience with ML / AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).

Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines.

Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.

Experience using Garak (or similar LLM red-teaming / vulnerability scanners) to identify model weaknesses and harden deployments.

Demonstrated experience implementing content filtering / moderation systems.

Solid skills working with structured and unstructured data and advanced feature engineering.

Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS / GCP acceptable).

Experience building APIs / microservices; containerization (Docker), orchestration (Kubernetes).

Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.

Good knowledge of security, data governance, and privacy best practices for AI systems.

Preferred / differentiating qualifications

Hands-on fine-tuning experience and parameter-efficient tuning methods.

Experience with multimodal models and retrieval-augmented multimodal pipelines.

Prior work on agentic safety, tool-use constraints, LLM application firewalls, or human-in-the-loop systems.

Familiarity with LangChain, LangGraph, Crew AI, or similar orchestration libraries.

Values & behaviours

AI-first thinking : consistently seeks AI-enabled solutions to business problems.

Data-driven mindset : makes decisions based on measurable insights and metrics.

Collaboration & agility : effective contributor in cross-functional, fast-paced teams.

Problem-solving orientation : looks beyond the obvious to unlock product and business value.

Business impact focus : designs solutions with measurable outcomes and real adoption.

Continuous learning : stays current with academic research, open-source tooling, and best practices.