Overview

🚀 Hiring: AI Engineering & Software Manager

Responsibilities

• Own the end-to-end delivery of AI initiatives, from architecture and prototyping through production deployment, optimization, and operations.
• Lead the development of enterprise-grade Generative AI and Agentic AI solutions with strong focus on scalability, security, reliability, and responsible AI.
• Build reusable AI capabilities including model adaptation, optimization frameworks, inference pipelines, and production-ready reference architectures.
• Provide technical direction across Generative AI, multimodal AI, representation learning, self-supervised learning, and model optimization.
• Transform promising AI prototypes into validated, reproducible, scalable, and production-ready enterprise solutions.
• Oversee AI architectures involving MCP, tool integrations, context management, secure data access, and enterprise systems.
• Establish rigorous model evaluation, benchmarking, validation, observability, and quality frameworks.
• Partner with infrastructure, platform, data engineering, security, privacy, legal, and business teams to operationalize AI at scale.
• Hire, mentor, and develop a high-performing team of AI engineers and applied AI/ML professionals.
• Identify high-value AI opportunities and translate business problems into impactful technical solutions.

Qualifications

• 🎓 Master’s from a top-tier/reputed institute preferred
• We are looking for an exceptional AI Engineering & Software Manager to lead the engineering, deployment, and scale-up of enterprise AI and Generative AI solutions.
• This is a high-impact leadership opportunity for a technically strong AI/ML engineering leader who brings a rare combination of deep software engineering expertise, hands-on Generative AI experience, architectural depth, and proven people leadership.
• The ideal candidate will have a strong track record of building complex enterprise systems, taking AI solutions from prototype to production, and establishing the engineering practices required to operate AI platforms reliably at scale.
• 13–18 years of overall experience in software engineering / IT.
• 6–7+ years of strong experience in core software engineering, systems architecture, or enterprise technology.
• 7+ years of AI/ML engineering experience, including significant recent experience with Generative AI and production AI systems.
• Proven experience leading technical teams across AI/ML, Generative AI, Foundation Models, Agentic AI, or ML platforms.
• Strong track record of taking technology initiatives from concept → engineering → production → scale.

Education

• Bachelor’s / Master’s / PhD in Computer Science, AI/ML, Electrical Engineering, Mathematics, Physics, or a related technical discipline.
• Master’s degree from a top-tier/reputed institute is strongly preferred.
• Kubernetes, containerized workloads, distributed systems, GPU infrastructure
• NVIDIA GPU environments, including A100/H100 and NVLink
• Model serving and inference using Triton Inference Server, vLLM, TGI, or equivalent
• Agentic AI frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent
• MLOps platforms including MLflow / Weights & Biases
• AI/LLM observability and evaluation tools such as Prometheus, Grafana, Arize, TruLens, or equivalent
• Strong understanding of CI/CD, secure enterprise deployment, middleware, configuration management, and production operations
• Experience across text, image, audio, video, speech, or multimodal AI is highly desirable
• Technically hands-on and comfortable going deep into architecture, AI systems, infrastructure, and production challenges.
• Strong at translating complex technical concepts into clear business outcomes.
• Comfortable operating within a global, matrixed enterprise environment.
• Highly execution-oriented with a strong ownership mindset.
• Experienced in building and mentoring high-performing engineering teams.
• Passionate about engineering excellence, reliability, scalability, security, and measurable business impact.
• Experience building and operating enterprise GenAI / Agentic AI platforms at scale.
• Strong expertise in LLM inference optimization, GPU utilization, latency, throughput, and cost optimization.
• Experience establishing AI governance, evaluation frameworks, responsible AI controls, and production quality gates.
• Demonstrated ability to move AI initiatives from research/prototype to production.
• Master’s degree from a top-tier institute.
• A stable career history with progressive growth, demonstrating sustained ownership and long-term delivery of complex technology programs.