Senior 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.