AI Engineer (DTV)

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.