Lead AI Engineer (Agentic AI Langchain)

REMOTE OPTION IS AVAILABLE
NO FRESHER WILL BE PREFERRED
We are looking for Lead AI Engineer (Agentic AI Langchain)as a Full Time for our IT Company.
Location- Remote
Experience- 6+ years
Budget- As per company standard
Notice Period- Immediate Joiner
Skills: Python, LangChain, LangSmith, RAG, Vector Databases, Agentic AI/Multi-Agent Orchestration, Temporal, Docker, Kubernetes, MLOps/LLMOps.
Senior AI Engineer
We are seeking a Senior AI Engineer to join the Tango project, an ambitious initiative to build an enterprise-grade Agent Development Platform (ADP). This role is central to our "Wave 1" objective: establishing a production-ready foundation that decouples core infrastructure from individual agent business logic. You will be responsible for building high-performance AI agents and the underlying SDK that powers them.
Key Responsibilities
Core AI Engineering & Delivery
- Agent Development: Design, build, and test production-grade agents (e.g., Lease Abstraction, CAM Reconciliation) using the Prodigy framework as a baseline.
- SDK & Gateway Engineering: Develop a lightweight Python SDK to interface between agents and the platform Control Plane, abstracting complex LLM routing to providers like AWS Bedrock [this line is cut off in the screenshot]
- Knowledge Engineering: Build and configure RAG (Retrieval-Augmented Generation) knowledge bases and GraphDB mappings to provide agents with secure, domain-specific context.
- Safety & Governance: Implement "Human-in-the-Loop" (HITL) escalation interfaces, confidence thresholds, and strict egress controls to prevent data exfiltration. Ensure all AI solutions comply with security, data privacy, and responsible-AI standards.
- Optimization: Configure "Agent Overrides" and token optimization strategies (e.g., "balanced" vs. "perfectionist" consumption) via configurable workflows.
- End-to-End Development: Lead the design, development, and deployment of advanced AI systems across Data Science and AI Engineering domains. Contribute hands-on to development activities, from experimentation and prototyping to production-grade implementation.
- Architecture & Pipelines: Architect and implement scalable AI pipelines and LLM-driven applications, including RAG, orchestration, and multi-agent systems.
- MLOps / LLMOps: Apply MLOps and LLMOps best practices for CI/CD, observability, evaluation, and continuous improvement of AI models and pipelines.
- Ecosystem Integration: Integrate AI services with enterprise platforms (e.g., Confluence, Jira, GitHub, CRM, ERP) and ensure seamless interoperability.
- Cross-Functional Collaboration: Collaborate with cross-functional engineering, data, and product teams to align technical solutions with client and business objectives.
- Innovation: Drive innovation through internal frameworks and accelerators.
Commercial / Presales
- Technical Authority: Serve as the technical authority during client engagements, ensuring architecture quality, scalability, performance, and reliability.
- Subject Matter Expertise: Act as a technical SME in presales conversations, translating business challenges into viable AI engineering solutions.
- Strategic Planning: Partner with delivery and account teams to shape long-term client engagement strategies based on engineering excellence.
Internal Resource Management & Team Building
- Mentorship & Standardization: Provide technical guidance to mid-level AI engineers and lead "Agent Factory" sessions to standardize decision logic and orchestration patterns. Mentor engineering peers and support knowledge sharing across teams and practices (e.g., contributing to Ciklum's AI Academy).
- Recruitment: Support recruitment efforts through technical assessments, interviews, and candidate evaluation.
- Cross-Skilling: Mentor team members and contribute to cross-skilling initiatives within the AI and Engineering service lines.
- Engineering Excellence: Uphold engineering excellence by promoting best practices, code quality, and delivery standards across projects.
Required Skills & Experience
- AI Ecosystem: Extensive experience with Python, LangChain, and LangSmith; familiarity with the Prodigy framework is a significant advantage.
- Agentic Intelligence: Proven track record building multi-step reasoning chains, tool-use protocols, and state management for autonomous agents.
- Data Architecture: Practical experience with Vector Databases (ChromaDB, PGVector) and building ETL "Data Engines" for secure agent consumption via MCP.
- Infrastructure: Understanding of containerization (Docker/K8s) and isolated runtimes such as Firecracker MicroVMs for agent sandboxing.
- Integration: Experience integrating AI solutions with enterprise identity providers like WorkOS for multi-tenancy.
- Workflow Orchestration: Experience with Temporal for managing long-running, multi-stage agentic workflows.
Desirable
- Domain Expertise: Familiarity with Commercial Real Estate workflows (Lease accounting, CAM reconciliation).
- Evals Literacy: Experience building programmatic "Evaluation Harnesses" and testing against "Golden Datasets" to measure hallucination rates.

Work arrangement
Yes