AI Engineer (DTV)

Job Description

Job Description

Job Description – AI Engineer (DTV)
Location: Remote

Role Summary
Hands-on AI Engineer who can design, build, and operationalize enterprise Agentic AI solutions. Rather than calling standard LLM APIs, you will build multi-agent ecosystems, implement cutting-edge protocol integrations (MCP, A2A), customize task-specific models, and engineer end-to-end full-stack applications with production-grade security, low latency, and cost control.

Key Responsibilities
1. Advanced Agent & Multi-Agent Engineering
Orchestration & Protocols: Build autonomous agents and multi-agent workflows using Model Context Protocol (MCP) for tool integration and Agent-to-Agent (A2A) protocols for task delegation.
Agent Capabilities: Design agent state management, short/long-term memory, dynamic planning, reasoning loops, and human-in-the-loop (HITL) fallback paths.
Model Optimization: Fine-tune, customize, and select models (LLMs vs. small task-specific models) based on empirical cost, latency, and accuracy metrics.
2. Enterprise RAG & Grounding
Advanced Retrieval: Design chunking, indexing, reranking, and hybrid search (vector keyword) pipelines with citation enforcement.
Data Privacy: Enforce tenant isolation and RBAC boundaries to prevent unauthorized data cross-contamination.
3. Automated Evaluation, MLOps & Guardrails
Systematic Evaluation: Build automated evaluation suites to benchmark groundedness, task completion, tool choice, safety, and agent behavior across updates.
Observability & CI/CD: Establish automated pipelines (Dev/Test/Prod), structured logging, tracing, token-cost tracking, and real-time operational alerting.
AI Guardrails: Implement active protections against prompt injection, jailbreaking, unsafe tool execution, and supply-chain vulnerabilities.
4. AI Security, Governance & Compliance
Threat Mitigation: Implement active guardrails against prompt injection, jailbreaking, vector store poisoning, insecure output handling, and supply-chain vulnerabilities.
Access & Data Governance: Enforce Zero-Trust architecture, Identity and Access Management (IAM), secrets management, and secure authorization controls across APIs, agents, and tool execution.
Control Plane: Develop AI control plane mechanisms, including policy enforcement, telemetry, emergency kill-switches, and automated workflows.
5. Full-Stack & Enterprise Architecture
End-to-End Application Engineering: Build responsive frontends and backend microservices using REST, GraphQL, and event-driven architectures.
Secure Enterprise Integration: Connect agents to enterprise databases, internal APIs, and Machine-to-Cloud (M2C) platforms while enforcing authorization-aware tool execution, rate limiting, and circuit breakers.

Required Qualifications
Experience: 8 years in software engineering with proven experience with at least 2 years in hands-on GenAI and AI-DLC applications to production.
Agentic Frameworks & Protocols: Deep experience with MCP, A2A, multi-agent systems, function/tool calling, RAG, taxonomy, and stateful agent orchestration.
Advanced AI/ML: Demonstrated ability in fine-tuning, embedding generation, hybrid RAG, model selection, and prompt/context engineering.
Engineering Rigor: Mastery of automated testing, secure coding (PII/PCI compliance), CI/CD, and performance troubleshooting (latency, throughput, cost).
Education: Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent practical experience.

Preferred Qualifications
Full-Stack & Systems: Strong hands-on proficiency in Python, modern web frontends, microservices, REST/GraphQL APIs, SQL/vector databases, containers, and cloud infrastructure.
Education: Master's degree in Computer Science, Engineering, Data Science, or equivalent practical experience.