Middle/ Senior AI Engineer

What you will build

Examples of solutions you will deliver include:

- Enterprise Support and Ops Agents

• Leverage AI and machine learning to learn from past tickets, emails, and logs to triage requests and monitor systems• Propose or execute actions with escalation and full audit trails

• Perform automated root cause analysis for complex enterprise environment

- Knowledge and Policy Agents

• Grounded Q&A over SOPs, manuals, and policies

• Citations, traceability, and access-controlled retrieval

• Security attestation agent to reduce administrative work and to guide users for policy compliance

• System onboarding agent to guide users and help in troubleshooting, price and cost estimation

- Workflow and Action Agents

• Multi-step orchestration (e.g. read request → analyze → fetch data approval → update system)→ draft response → trigger

Responsibilities

1. Agentic Solution Design

- Design goal-driven agents that decompose tasks, select tools, manage state, and recover from failures

- Implement agent patterns such as planner–executor, coordinator–worker, reflection/self-check, and human-in-the-loop decision gates.

2. Platform Implementation: You will work across one or more of the following platforms:

- Microsoft Copilot Studio

• Build copilots with plugins, connectors, and enterprise guardrails

- Google AgentSpace

• Build agents integrated with other applications or tools (e.g. ITSM, SIEM, Workflow management system)

• Orchestrate multi-step workflows and API-driven actions

- AWS Bedrock

• Design secure agentic workflows using Bedrock models and tools

- Dataiku

• Operationalize agents within analytics, ML pipelines, and business workflows

- An operational Hybrid Machine Learning and LLM model to support smart Digital Platform and Security Operations

• ML models detect anomalies → LLM explains them• ML predicts incidents → LLM drafts remediation and healing steps

• ML scores risk → LLM supports human decision-making

3. Retrieval and Grounding (RAG)

- Design enterprise RAG pipelines including ingestion, chunking, embeddings, retrieval, reranking, and citation.

- Ensure retrieval respects role-based access control and data classification.

4. Evaluation, Observability, and Operations

- Build evaluation frameworks for non-deterministic systems: task success metrics, grounding checks, hallucination detection, and regression tests.

- Implement observability for prompts, retrieval, and tool calls.

- Own solutions from POC through MVP and production.

5. Security, Governance, and Responsible AI

- Enforce least-privilege tool access, audit logging, secrets management, prompt-injection defenses, and safe action boundaries.

- Design human approval checkpoints for high-risk or irreversible actions.

- Comply with enterprise AI governance and ethics requirements.

Requirements

✓ 3-5+ years professional software engineering experience.

✓ Hands-on experience building GenAI or LLM systems beyond basic chatbots i.e. a hybrid Machine Learning and LLM model in an operational platform

✓ Strong understanding of agentic concepts: tool/function calling, state and memory, planning and execution loops.

✓ Practical experience with at least one of: Microsoft Copilot Studio, Google AgentSpace, AWS Bedrock, or Dataiku.

✓ Experience with vector databases and embeddings.

Preferred skills and experiences:

✓ Familiarity and experience with UiPath is an added advantage

✓ Familiarity and experience with project management concepts and methodologies is preferred