Job Title: Principal Architect - LLM Agents, SLM & Multi-Agent Frameworks
Summary: We are looking for a visionary Principal Architect to lead the end‑to‑end design and delivery of AI‑powered systems centred around LLM/SLM agents and multi-agent frameworks. This role demands a blend of deep software engineering expertise and strong data science foundations – especially in applying modern ML/DL techniques, fine-tuning SLMs, working with Knowledge Graphs (KGs) in enterprise-grade graph databases, implementing RAG variants with LLMs, and architecting agent-driven solutions using modern agentic frameworks.
The ideal candidate will bring 14+ years of engineering experience, including 5+ years in AI/ML, with demonstrable experience architecting distributed systems, full-stack applications including frontend frameworks and backend technologies like Node.js and Python, and cloud-native platforms such as AWS, Azure, or GCP. In addition to strong application development skills, the candidate should bring hands-on expertise in data integration pipelines, model development, KG-driven reasoning, agentic workflows, and ML Ops.Strong problem-solving skills, excellent communication, a passion for continuous learning and mentoring junior engineers are essential.
Responsibilities:
o Lead Architectural Design:
o Define and evolve the overall architecture for LLM-powered agents and multi-agent systems that optimize agent economics over time.
o Design highly scalable, resilient microservices and distributed workflows.
o Ensure seamless integration of AI Agents with other core systems, knowledge repositories and databases (structured + unstructured).
o Drive the development of APIs and SDKs for broader ecosystem adoption.
o Model Building, SLM Development & LLM/SLM Fine‑tuning:
o Collaborate with data scientists and ML engineers to fine-tune, distil, and evaluate SLMs/LLMs and optimize SLMs/LLMs for specific tasks and domains.
o Apply techniques such as RAG, knowledge graph completion, retrieval optimization, embeddings tuning, and model compression.
o Hands-on experience with agent frameworks like MAF, Autogen, AWS Agent Framework, LangGraph etc.
o Build and maintain evaluation pipelines using tools like Datadog, LangSmith, MLFlow, or equivalent
o Stay abreast of the latest advancements in LLM research and development.
o Knowledge Graphs & Contextual Intelligence
- Lead the design and integration of Knowledge Graphs, ontologies, and enterprise context graphs to enhance agent reasoning.
- Work on entity resolution, relationship extraction, graph embeddings, and graph-based retrieval.
- Architect KG-backed workflows to improve grounding, reduce hallucinations, and enable enterprise-aware agent behaviour.
o Prompt Engineering & LLM Integration:
o Develop and refine effective prompting strategies to maximize the performance of LLMs.
o Design and implement mechanisms for safe and reliable LLM integration.
o Address challenges related to bias, hallucinations, and other potential LLM limitations.
o ML Ops & Observability:
o Establish and maintain robust ML Ops practices, including CI/CD pipelines, model versioning, feature stores, model registries and experiment tracking.
o Implement comprehensive monitoring and observability solutions to track model performance, identify anomalies, and ensure system stability.
o Data Engineering & Pipelines:
o Architect and optimize data pipelines for ingestion, transformation, KG construction, and model training datasets.
o Ensure data governance, lineage, and high-quality semantics across DS workflows.
o Full-Stack Expertise:
o Possess deep expertise across the full stack, including:
o Frontend (optional): React, Angular, Vue.js, or similar frameworks.
o Backend: Node.js, Python (with frameworks like Flask, Django, or FastAPI), Java, or other relevant languages.
o Database: SQL (MySQL, PostgreSQL), NoSQL (MongoDB, Cassandra), and experience with database design, optimization, and management.
o Cloud Platforms: Any 02 out of AWS, Azure, GCP (experience with serverless computing, containerization, and cloud-native technologies is a must).
o Team Leadership & Mentorship:
o Guide and mentor junior engineers in best practices for development and deployment of agents.
o Foster a culture of innovation, collaboration, and continuous learning within the team.
Qualifications:
- Proven Experience: 14+ years of experience in software engineering with a strong focus on AI/ML for at least 05 years.
· SLM/LLM Expertise: Proven experience in training/fine-tuning SLMs/LLMs (OpenAI, Claude, Llama, Mistral, etc.), including optimization, distillation, and RAG
· Knowledge Graph Skills: Experience in designing and working with KGs, graph databases, ontologies, graph embeddings, and contextual reasoning
· Architectural Strength: Ability to design large-scale distributed systems; expert in cloud-native and microservices architecture.
- Data Engineering Skills: Strong experience with data modeling, pipelines, and data governance.
- ML Ops & Observability: Expertise in CI/CD for ML, observability, model monitoring, and production ML workflows.
- Full-Stack & Cloud Competency: Strong hands-on experience with frontend, backend, and cloud technologies.
- Communication & Collaboration: Excellent communication, collaboration, and leadership skills.
- Strong Problem-Solving & Analytical Skills: Ability to analyze complex problems and develop innovative solutions.
- Continuous Learning: Passion for learning and staying up to date with the latest advancements in AI/ML.
コグニザントのコミュニティ：
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私たちについて：
コグニザント（NASDAQ: CTSH）は、AI builderおよびテクノロジーサービスプロバイダとして、AI投資を企業価値へとつなげるフルスタックのAIソリューションを提供しています。業界、業務プロセス、エンジニアリングに関する深い専門性を強みに、各企業固有のコンテキストをテクノロジーシステムに組み込み、人の力を最大限に引き出すとともに、具体的な成果の創出と、急速に変化する世界におけるグローバル企業の競争力維持を支援します。詳しくは、当社ウェブサイト www.cognizant.com をご覧ください。
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免責事項：
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