Software Engineer

Job Summary Key Responsibilities 1. End-to-End AI Implementation

• Own 3-4 complete AI delivery cycles: discovery, requirements, solution design, build, test, and production deployment.
• Translate ambiguous business problems into concrete AI solutions with clear success metrics.
• Define and manage delivery milestones across cross-functional stakeholders.
• Conduct design reviews, code reviews, and production readiness assessments.

2. Architecture & System Design

• Design multi-agent pipelines with clear separation of concerns, fault isolation, and observability.
• Define data flows, integration patterns (REST, gRPC, event-driven), and storage strategies for AI workloads.
• Present technical proposals to senior members -technical leads, Staff/Principal Engineers .

3. Agentic AI - Build & Orchestration

• Design, build, and deploy intelligent agents for complex, multi-step reasoning and task execution.
• Implement agent orchestration using Google Workflows, Temporal, LangChain, LangGraph, CrewAI, or equivalent frameworks.
• Deploy agents on GCP (Vertex AI Agent Builder, Cloud Run, GKE) or AWS / Azure equivalents.
• Integrate agents with external tools, APIs, databases, and enterprise systems via function calling and tool-use patterns.
• Implement ReAct, Plan-and-Execute, and other advanced prompting and reasoning strategies.

4. Agent Governance & Enterprise Readiness

• Design immutable, structured audit logs capturing agent decisions, tool invocations, and data accessed - enabling full traceability.
• Implement content safety policies, permission scopes, and fine-grained access controls governing agent behaviour.
• Architect HITL checkpoints for high-risk decisions, escalation flows, and approval gates.
• Human-in-the-Loop (HITL):
• Design secure, reliable A2A protocols including message schemas, authentication, and failure handling.
• Agent-to-Agent (A2A) Communication:
• Establish agent identity frameworks (service accounts, OAuth, signed tokens) for secure system interactions.
• Build or adopt agent SDKs; maintain a centralised agent registry for discovery, versioning, and lifecycle management.
• Agent SDK & Registry:
• Define and instrument evaluation frameworks covering task success rate, latency, token cost, and output quality.
• Evaluation Metrics:
• Implement runtime monitoring for unexpected agent behaviour, runaway loops, and out-of-distribution outputs.
• Anomaly Detection:
• Develop risk scoring models to dynamically assess and throttle agent actions based on context and potential impact.
• Risk Scoring:

5. Generative AI Engineering

• Design and implement RAG (Retrieval-Augmented Generation) pipelines with vector stores (Pinecone, Weaviate, pgvector, etc.).
• Fine-tune, prompt-engineer, and evaluate foundation models (Gemini, GPT-4, Claude, Llama) for domain-specific tasks.
• Optimise inference costs through caching, batching, model routing, and quantisation strategies.
• Build evaluation harnesses to benchmark model quality, factuality, and safety at scale.

Required Qualifications Experience

• 5-6 years of software engineering experience, with at least 4 years focused on AI/ML systems.
• Proven experience designing and deploying complex, production-grade systems at scale.

Technical Skills

• Agentic AI: LangChain, LangGraph, CrewAI, AutoGen, Google Agent Development Kit (ADK), or similar.
• Cloud Platforms: GCP (Vertex AI, Cloud Run, GKE, Workflows) - AWS or Azure equivalents acceptable.
• Orchestration: Google Workflows, Apache Airflow, Prefect, or equivalent.
• LLM APIs: Gemini, OpenAI, Anthropic Claude, Mistral, or open-source models (Llama, Mistral).
• Vector DBs & RAG: Pinecone, Weaviate, Qdrant, pgvector, or Elasticsearch.
• Languages: Python (primary); Go, Java, C#, TypeScript a plus.
• Infra & DevOps: Docker, Kubernetes, Terraform, CI/CD pipelines (GitHub Actions, Cloud Build).
• Observability: OpenTelemetry, Prometheus, Grafana, or equivalent for AI workload monitoring.
• Security: OAuth 2.0, mTLS, IAM policies, secret management (Vault, Secret Manager).

Nice to Have

• Experience with Google Vertex AI Agent Builder or similar managed agent platforms.
• Contributions to open-source AI/agent frameworks.
• Familiarity with AI safety, responsible AI, and ethics frameworks.
• Experience with multi-modal models (vision, audio, code).
• Published research, conference talks, or technical blog posts on AI topics.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.