Require an AI Software Engineer in Gurgaon

Key Responsibilities Design and build agentic AI platform components including agents, tools, workflows, and integrations with internal systems. Implement observability across the AI lifecycle: tracing, logging, metrics, and evaluation pipelines to monitor agent quality, cost, and reliability. Translate business problems into agentic AI solutions by collaborating with product, SMEs, and platform teams on data, model, and orchestration requirements. Develop and maintain data pipelines, features, and datasets for training, evaluation, grounding, and safety of LLM-based agents. Lead experimentation and benchmarking: Testing of prompts, models, and agent workflows; analyze results and drive iterative improvements. Implement guardrails, safety checks, and policy controls across prompts, tool usage, access, and output filtering to ensure safe and compliant operation. Create documentation, runbooks, and best practices; mentor peers on agentic AI patterns, observability-first engineering, and data/ML hygiene. Requirements Strong programming experience in Python (preferred) or equivalent languages Solid understanding of LLM / GenAI fundamentals: prompting, embeddings, vector search, RAG, and basic agentic patterns (tool use, planning, orchestration). Experience running production systems or data pipelines on AWS / Azure / GCP, using containers, serverless, and managed storage/services. Hands-on familiarity with observability tools (OpenTelemetry, Prometheus, Grafana, ELK, etc.) across logs, metrics, and traces. Comfort working with structured and unstructured data; strong SQL plus experience with Pandas / Spark / dbt or similar frameworks. Ability to reason clearly about reliability, performance, and cost trade-offs. Strong collaboration and communication skills; ability to translate complex concepts for platform, product, data, security, and compliance teams. Qualifications 26 years of experience in software engineering, data engineering, ML engineering, data science, MLOps roles. Bachelors or Masters degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Experience with CI/CD, code reviews, and modern engineering best practices. Key Responsibilities Design and build agentic AI platform components including agents, tools, workflows, and integrations with internal systems. Implement observability across the AI lifecycle: tracing, logging, metrics, and evaluation pipelines to monitor agent quality, cost, and reliability. Translate business problems into agentic AI solutions by collaborating with product, SMEs, and platform teams on data, model, and orchestration requirements. Develop and maintain data pipelines, features, and datasets for training, evaluation, grounding, and safety of LLM-based agents. Lead experimentation and benchmarking: Testing of prompts, models, and agent workflows; analyze results and drive iterative improvements. Implement guardrails, safety checks, and policy controls across prompts, tool usage, access, and output filtering to ensure safe and compliant operation. Create documentation, runbooks, and best practices; mentor peers on agentic AI patterns, observability-first engineering, and data/ML hygiene. Requirements Strong programming experience in Python (preferred) or equivalent languages Solid understanding of LLM / GenAI fundamentals: prompting, embeddings, vector search, RAG, and basic agentic patterns (tool use, planning, orchestration). Experience running production systems or data pipelines on AWS / Azure / GCP, using containers, serverless, and managed storage/services. Hands-on familiarity with observability tools (OpenTelemetry, Prometheus, Grafana, ELK, etc.) across logs, metrics, and traces. Comfort working with structured and unstructured data; strong SQL plus experience with Pandas / Spark / dbt or similar frameworks. Ability to reason clearly about reliability, performance, and cost trade-offs. Strong collaboration and communication skills; ability to translate complex concepts for platform, product, data, security, and compliance teams. Qualifications 26 years of experience in software engineering, data engineering, ML engineering, data science, MLOps roles. Bachelors or Masters degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Experience with CI/CD, code reviews, and modern engineering best practices.