About Signify
Through bold discovery and cutting-edge innovation, we lead an industry that is vital for the future of our planet: lighting. Through our leadership in connected lighting and the Internet of Things, we're breaking new ground in data analytics, AI, and smart solutions for homes, offices, cities, and beyond.
At Signify, you can shape tomorrow by building on our incredible 125+ year legacy while working toward even bolder sustainability goals. Our culture of continuous learning, creativity, and commitment to diversity and inclusion empowers you to grow your skills and career.
Join us, and together, we’ll transform our industry, making a lasting difference for brighter lives and a better world.
More about the role
We are looking for an MLOps Lead - Principal ML Engineer to lead the design, engineering and productionization of enterprise ML, GenAI and agentic AI platforms for Signify Advanced Analytics. The role will own end-to-end ML engineering across AWS and Snowflake, from data pipelines, model training and deployment to monitoring, governance, reliability, cost control and reuse across markets and business functions. The successful candidate will combine hands-on ML engineering depth with platform thinking, agentic AI architecture, LLMOps, technical leadership and strong stakeholder communication.
You’ll be part of the Signify’s AI & Advanced Analytics team, based in Bangalore.
What you’ll do
- Build and operate GenAI and agentic AI systems using RAG, embeddings, vector search, prompt chaining, tool-using agents, multi-agent orchestration and guardrails.
- Lead ML engineering and MLOps for Advanced Analytics, including architecture, delivery standards, reusable patterns, platform adoption and production support.
- Own productionization of ML and GenAI solutions across forecasting, marketing mix modeling, customer segmentation, contract intelligence, IoT analytics and enterprise copilots.
- Design and implement scalable ML pipelines across data ingestion, feature engineering, training, evaluation, deployment, monitoring, retraining and model retirement.
- Define target architecture for enterprise GenAI platforms, including agent registry, MCP registry, agent-to-agent communication, observability, safety, lifecycle management and reusable integration patterns.
- Engineer reliable agent runtimes with retry logic, timeout handling, fallback strategies, deterministic execution where required, idempotency, execution tracing and production SLOs.
- Design and own evaluation frameworks for ML, GenAI and agentic AI systems, including offline and online evals, task success metrics, hallucination checks, latency, cost, quality and regression benchmarks.
- Establish LLMOps practices covering prompt testing, model evaluation, data curation, fine-tuning workflows, safety testing and continuous improvement from production feedback.
- Optimize GenAI workloads for latency, throughput and cost using model routing, caching, batching, autoscaling, token usage monitoring and fit-for-purpose model selection.
- Build reusable AI platform services, APIs, microservices and integration patterns that allow internal teams to safely consume ML and GenAI capabilities.
- Create observability dashboards for model performance, data drift, agent traces, token usage, latency, reasoning paths, production failures and business impact.
- Integrate ML and GenAI workloads with Snowflake using Snowpark, APIs, storage integrations and Cortex where relevant.
- Use AWS services such as SageMaker, Bedrock, Lambda, Fargate, EC2, S3, SNS and SQS to deliver reliable cloud-native ML platforms.
- Establish CI/CD practices for ML, model versioning, automated testing, reproducible deployments, data quality checks, access control and deployment readiness gates.
- Own experimentation infrastructure, including A/B testing, offline evaluation and production feedback loops for ML and GenAI products.
- Guide ML engineers and data scientists on architecture, coding practices, experimentation, model evaluation, deployment readiness and operational excellence.
- Partner with business, digital, CDIO and analytics stakeholders to translate business problems into scalable AI products and measurable outcomes.
- Evaluate emerging platforms and tools such as Snowflake Cortex, Bedrock Agents, open-source LLMs, agent frameworks, MCP-based integrations and AI observability platforms, and recommend adoption paths.
- Champion responsible AI practices covering explainability, fairness, transparency, auditability, access control, privacy, security and safe use of enterprise data.
- Present technical designs, trade-offs, risks and business outcomes clearly to senior stakeholders.
What you’ll need
- B.Tech / Masters in Computer Science, Computer Engineering, Data Science or equivalent experience. 12+ years of experience in ML engineering, MLOps, advanced analytics or applied data science.
- Strong hands-on experience in Python and SQL, with practical knowledge of ML libraries and frameworks such as scikit-learn, TensorFlow, Keras, pandas, NumPy, Polars and Snowpark.
- Deep experience in MLOps and ML lifecycle management, including CI/CD for ML, model deployment and serving, monitoring, data pipelines, model evaluation and governance.
- Hands-on experience with AWS ML and cloud services, especially SageMaker, Bedrock, Lambda, Fargate, EC2, S3, SNS, SQS and Docker.
- Strong Snowflake experience, including Snowflake APIs, Snowpark, storage integrations and practical evaluation or use of Cortex AI / Cortex Agents.
- Experience building GenAI solutions with LLMs, RAG, embeddings, vector search, prompt engineering, agentic AI and multi-agent systems.
- Experience with agent frameworks and tools such as AG2 / AutoGen, Agno, llama.cpp, MCPs, agent registry, MCP registry, Stackloc or equivalent technologies.
- Experience with agent evaluation, LLMOps, observability and production monitoring for GenAI systems.
- Strong understanding of production AI platform concerns such as APIs, authentication, rate limiting, cost control, reliability, security, privacy and responsible AI.
- Experience with big data and streaming technologies such as Hadoop, Hive, Spark, Kafka and Airflow.
- Strong understanding of ML fundamentals, time-series forecasting, NLP, optimization, marketing mix modeling, segmentation, experimentation and statistical modeling.
- Demonstrated experience taking AI solutions from prototype to production across multiple markets, functions or business units.
- Ability to lead small technical teams, mentor engineers and data scientists, manage delivery priorities and work in multidisciplinary environments.
- Excellent communication skills, with the ability to explain ML architecture, model behavior, risks and business impact to technical and non-technical stakeholders.
Preferred / good to have
- Hands-on exposure to Kubernetes, Terraform, MLflow, Weights & Biases, LangSmith, Langfuse, BentoML, vLLM, Ray, Argo or equivalent MLOps / LLMOps tools.
- Experience deploying agents and ML services as scalable microservices with container orchestration, autoscaling, observability and incident response practices.
- Experience with fine-tuning, model distillation, quantization, dynamic batching or other techniques for cost-aware and latency-aware AI workloads.
- Familiarity with MCP, A2A orchestration, tool registries, API gateways, model gateways and secure exposure of enterprise data and actions to LLM agents.
- Experience defining AI governance, evaluation rubrics, release gates and lifecycle controls for enterprise AI products.
Technology stack
Area
Tools / capabilities
Languages
Python, SQL
Cloud and data
AWS SageMaker, Bedrock, Lambda, Fargate, EC2, S3, SNS, SQS, Snowflake, Snowpark, Snowflake APIs, Postgres, pgvector
MLOps and LLMOps
CI/CD for ML, model serving, model registry, monitoring, evaluation, data quality, retraining, governance, MLflow / W&B / LangSmith or equivalents
GenAI and agents
LLMs, RAG, embeddings, vector search, prompt engineering, tool calling, multi-agent systems, MCP, A2A, agent registry, MCP registry, guardrails
Engineering
Docker, FastAPI, Streamlit, APIs, microservices, Kafka, Airflow, Kubernetes and Terraform preferred
ML and analytics
Forecasting, NLP, segmentation, marketing mix modeling, optimization, experimentation, statistical modeling
What you’ll get in return…
- Opportunity to lead enterprise-scale ML and GenAI productionization for high-impact business problems across functions and markets.
- Strong international exposure through reusable AI products, market rollouts and collaboration with business, digital and technology teams.
- A platform to shape MLOps, LLMOps, GenAI engineering standards and responsible AI adoption for Advanced Analytics.
- Continuous learning opportunities, mentoring, coaching and stretch assignments in a fast-moving AI landscape.
Everything we’ll do for you
You can grow a lasting career here. We’ll encourage you, support you, and challenge you. We’ll help you learn and progress in a way that’s right for you, with coaching and mentoring along the way. We’ll listen to you too, because we see and value every one of our 27,000+ people. We believe that a diverse and inclusive workplace fosters creativity, innovation, and a full spectrum of bright ideas. With a global workforce present in 70+ countries, we are dedicated to creating an inclusive environment where every voice is heard and valued, helping us all achieve more together.
Come join us, and together we can light up the future.
