Senior Artificial Intelligence/Machine Learning Engineer

Responsibilities

• 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 and Anthropic
• 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
• 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
• 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

Requirements

• 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

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