AI Agentic Engineer

Responsibilities

• Agentic System Design & Engineering:
  Architect, build, and deploy advanced AI agents capable of autonomous reasoning, decision-making, and self-directed task execution. Design and implement complex, multi-step agentic workflows that integrate with enterprise APIs, data sources, and platforms.
• RAG and Grounding Implementation:
  Develop robust RAG pipelines to ground agent responses in factual, reliable data. This includes managing the full lifecycle from data ingestion and vectorization to retrieval and citation.
• Tooling and Integration:
  Build and maintain the "tools" that agents use to interact with the digital world. Create secure, well-documented tool interfaces for internal services, databases, and third-party APIs.
• Evaluation, Guardrails & Safety:
  Design and implement comprehensive evaluation frameworks to measure agent performance, accuracy, and reliability.
• Develop and enforce safety guardrails, policy checks, and fallback mechanisms to ensure agents operate safely and predictably in production environments.
• Optimization and Productionization:
  Debug, monitor, and optimize agentic systems for latency, cost, and efficiency. Own the end-to-end deployment process, including CI/CD, structured logging, and incident response for AI systems.

• Core Engineering & Programming:
  Strong software engineering fundamentals with expert-level proficiency in Python. Experience with Java, Go, or Type Script is a strong plus.
• LLM & GenAI Application Development:
  Proven, hands-on experience building and deploying production-grade applications using Large Language Models (LLMs) like GPT, Claude, or Gemini.
• This must go beyond simple API calls and include experience with tool/function-calling, structured outputs, and evaluation.
• Agentic AI Frameworks & Orchestration:
  Demonstrable expertise in designing and implementing agentic workflows using frameworks like Lang Graph, Semantic Kernel, Auto Gen, or similar.
• Experience with multi-agent systems, planning, and autonomous execution is critical.
• RAG (Retrieval-Augmented Generation):
  Deep, practical knowledge of building and optimizing RAG pipelines. This includes data ingestion, various chunking strategies, embeddings, vector databases (e.g., Pinecone, Chroma, FAISS), and hybrid search/reranking.
• Production & MLOps:
  Experience with production engineering practices, including building scalable APIs (REST, RPC), microservices, CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, Azure, or GCP).

• Agentic System Design & Engineering:
  Architect, build, and deploy advanced AI agents capable of autonomous reasoning, decision-making, and self-directed task execution. Design and implement complex, multi-step agentic workflows that integrate with enterprise APIs, data sources, and platforms.
• RAG and Grounding Implementation:
  Develop robust RAG pipelines to ground agent responses in factual, reliable data. This includes managing the full lifecycle from data ingestion and vectorization to retrieval and citation.
• Tooling and Integration:
  Build and maintain the "tools" that agents use to interact with the digital world. Create secure, well-documented tool interfaces for internal services, databases, and third-party APIs.
• Evaluation, Guardrails & Safety:
  Design and implement comprehensive evaluation frameworks to measure agent performance, accuracy, and reliability.
• Develop and enforce safety guardrails, policy checks, and fallback mechanisms to ensure agents operate safely and predictably in production environments.
• Optimization and Productionization:
  Debug, monitor, and optimize agentic systems for latency, cost, and efficiency. Own the end-to-end deployment process, including CI/CD, structured logging, and incident response for AI systems.

• Generic Managerial Skills (if any):
  Problem-Solving & Critical Thinking:
  Ability to analyze complex, ambiguous problems and design innovative, practical solutions. Thrives in navigating the uncertainty inherent in emerging AI technologies.
• Collaboration & Communication:
  Excellent communication skills with the ability to articulate complex technical concepts to both technical and non-technical stakeholders. Proven experience working cross-functionally with product, research, and infrastructure teams.
• Ownership & Leadership: A bias for action and a strong sense of ownership. Capable of driving projects from conception to completion, mentoring junior engineers, and helping to define and influence AI strategy and best practices.

Base Salary Range : $100,000 to $120,000 Per Annum

• Discretionary Annual Incentive
• Comprehensive Medical Coverage:
  Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans
• Family Support:
  Maternal & Parental Leaves
• Insurance Options:
  Auto & Home Insurance, Identity Theft Protection
• Convenience & Professional Growth:
  Commuter Benefits & Certification & Training Reimbursement
• Time Off:
  Vacation, Time Off, Sick Leave & Holidays
• Legal &…