AI Solutions Principal Engineer (Secunderabad)

Overview

As an AI Principal Engineer specializing in Agentic AI enablement, you will lead the design and delivery of production-grade agent capabilities built on the enterprise AI Backbone across cloud and edge environments
- across supply-chain and global functions. You will own end-to-end delivery of key agent modules and integration patterns (MCP/tooling), establish strong evaluation and regression discipline, and drive adoption by partnering with transformation teams, BU, platform engineering, and enterprise application owners. You serve as a technical anchor for the workstream-translating ambiguous business workflows into measurable agent outcomes, proactively identifying risks, proposing options/tradeoffs, and ensuring solutions scale across domains. Responsibilities

1) Architectural Leadership & Strategic Execution (40%)
- Design and Architect transformative agent systems that enable organization-wide scaling, establishing new paradigms in agent architecture that become company standards. (Lead/Execute)
- Pioneer novel agent patterns (tool-use orchestration, multi-agent systems, advanced memory architectures) that dramatically improve performance across the enterprise. (Lead/Execute)
- Transform ambiguous business problems into elegant technical solutions with 10x efficiency gains through creative approaches to system design. (Lead)
- Optimize critical performance metrics beyond standard benchmarks, creating breakthrough improvements (90th percentile latency reduction, 50%+ token efficiency, near-perfect tool-call reliability). (Execute/Lead)
- Establish architectural governance that propagates excellence across teams and projects. (Lead) 2) Advanced Evaluation & Quality Engineering (20%)
- Design scientifically rigorous evaluation frameworks that uncover non-obvious failure modes and edge cases others miss. (Lead/Execute)
- Create organization-level evaluation standards and platforms that scale across multiple teams and projects. (Lead)
- Innovate on automated testing methodologies that dramatically increase code quality while reducing QA overhead. (Execute/Lead)
- Perform sophisticated statistical analysis of system behaviors to predict quality issues before they manifest. (Execute)
- Establish early warning systems for emerging failure patterns. (Execute/Lead) 3) Model Architecture & Routing Innovation (15%)
- Architect intelligent routing systems that autonomously optimize for cost, latency, and quality trade-offs. (Lead/Execute)
- Pioneer novel approaches to model selection, fine-tuning, and prompt engineering that set new performance standards. (Lead)
- Create optimization algorithms that continuously improve routing decisions based on real-time feedback loops. (Execute/Lead)
- Develop proprietary techniques for model evaluation that provide competitive advantage. (Execute/Lead) 4) Advanced Integration & Ecosystem Development (15%)
- Design scalable integration architectures that become enterprise standards for AI/app connectivity. (Lead)
- Create abstraction layers that dramatically simplify how teams connect AI capabilities to enterprise systems. (Execute/Lead)
- Establish next-generation integration patterns that anticipate future technology directions and enable seamless adoption. (Lead)
- Develop tooling that accelerates integration velocity across the entire organization. (Execute/Lead) 5) Organizational Multiplier & Innovation Leadership (10%)
- Serve as technical visionary, elevating the entire AI organization's capabilities through knowledge transfer and mentorship. (Lead)
- Anticipate industry shifts and position the organization to capitalize on emerging technological opportunities. (Lead)
- Create internal communities of practice that accelerate knowledge sharing and collective innovation. (Lead)
- Represent the company's technical excellence externally through publications, speaking engagements, and industry contributions. (Lead)
- Drive cross-functional initiatives that break down silos and create new organizational capabilities. (Lead/Execute) Qualifications

Minimum Qualifications
- Bachelor's/Master's in CS/AI/ML or equivalent experience.
- Expertise in ML (structured and unstructured data) development and engineering
- Proven experience shipping LLM/agent solutions to production with measurable quality and operational practices. Required Expertise
- 10+ years of Software Development Experience.
- Advanced Software Engineering: Python (and Java) mastery with distributed systems expertise performance optimization (profiling, parallelization) architecture patterns (e.g., FastAPI, asyncio, Pydantic)
- LLM & Agent Systems: Multi-agent orchestration (LangChain, LangGraph, CrewAI) advanced prompt engineering custom agent memory architectures model optimization techniques
- Evaluation Framework Development: Statistical evaluation design (confidence intervals, power analysis) benchmark creation instrumentation frameworks (e.g., MLflow, Arise) regression testing systems
- ML Operations: Production deployment pipelines (Docker, Kubernetes, Ray) model registry management scaled inference optimization GPU utilization optimization
- Enterprise Integration: Enterprise connector development scalable API architectures data pipeline engineering (Kafka, gRPC, Redis) authorization protocol implementation
- Observability Engineering: Telemetry system design (Prometheus, OpenTelemetry) automated anomaly detection distributed tracing performance dashboarding (Grafana)
- System Architecture: Microservice design patterns high-throughput event processing fault-tolerance implementation horizontal scaling architectures
- Technical Leadership: Architecture governance systems engineering standards development build-vs-buy evaluation frameworks technical roadmap creation Good-to-have Skills
- Full-stack dev experience on modern stack
- Modelling User Interactions with AI Systems Modeling multi-agent behaviour loops with tools like Temporal
- Agentic memory Patterns and usage with tools like MEM0 and Temporal
- Experience with Agentic RAG Domain level Semantic Layer Designs with Graph and Vector DBs Differentiating Competencies Required
- Identify any differentiating behaviors, leadership skills or soft skills required for success in the role.
- Ownership: drives outcomes end-to-end for a workstream area (not just tasks)
- Collaboration & customer focus: influences stakeholders to deliver workflow value and adoption
- Communication & adaptability: executive-ready clarity on progress, risks, and evaluation evidence
- Proactiveness & initiative anticipates constraints, proposes options/tradeoffs early
- Strategic thinking: contributes to roadmap sequencing and reusable patterns across domains Key Differentials :
- Demonstrates proven history of creating solutions with order-of-magnitude improvements over standard approaches
- Possesses rare combination of deep technical expertise and strategic business understanding
- Creates solutions that scale beyond their direct involvement (leveraged impact)
- Consistently elevates the performance of teams and individuals around them
- Identifies and solves problems others haven't recognized yet
- Maintains extraordinary productivity while ensuring knowledge transfer
- Balances technical perfectionism with pragmatic business value
- Communicates complex technical concepts effectively to both technical and non-technical stakeholders Apply on Kit Job: kitjob.in/job/4m96w1