About Brightly

Brightly is a global leader in intelligent, cloudbased asset lifecycle management, empowering 12,000+ clients with predictive insights to optimize maintenance, energy, and capital planningbacked by Siemens. Our products help schools, hospitals, cities, and manufacturers keep critical infrastructure running efficiently and sustainably.

The Opportunity

Were looking for a Senior Machine Learning Engineer to lead LLMpowered application developmentfrom prototype to productionon AWS. Youll design robust ML/LLM services that power search, recommendations, copilots, and workflow automation in Brightlys platform, partnering closely with product, data, and engineering teams. Responsibilities and skill expectations reflect current industry practice for senior ML/LLM engineers, including endtoend model lifecycle ownership, productiongrade code, and MLOps.

What youll do

Build LLM applications: Design and implement RAG pipelines, prompt orchestration, tools/agents, safety/guardrails, and evaluation harnesses; instrument for latency, cost, and quality. (Guided by current LLM engineer role practices.)

Own the ML lifecycle: Data curation, feature engineering, training/finetuning (LoRA/QLoRA), A/B testing, deployment, monitoring, and continuous improvement of models and prompts.

Productionize on AWS: Ship scalable services on EKS/ECS/Lambda; leverage SageMaker, Bedrock, EMR, MSK, Step Functions; apply observability (CloudWatch/OpenTelemetry) and cost controls. (Duties aligned to modern AWS ML roles.)

Scale training inference: Use distributed training (FSDP/DeepSpeed), quantization, caching, vector databases, and GPU/Inferentia for performance and efficiency.

MLOps governance: Establish CI/CD for models (MLflow/Kedro/SageMaker Pipelines), model/version registries, data and prompt lineage, evaluation gates, and responsibleAI controls. (Aligned with contemporary MLOps templates.)

Partner across Brightly:



Translate assetmanagement use cases into ML/LLM solutions; collaborate with product managers and UX to ship customervisible features that measurably improve reliability, safety, and sustainability.

Mentor lead: Provide technical leadership, review designs/PRs, and raise the bar on ML engineering excellence across the team. (Common senior ML expectations.)

Perform Exploratory Data Analysis (EDA) on structured, semistructured, and unstructured datasets to identify patterns, correlations, feature importance, and data quality issues.

(Consistent with ML engineer responsibilities to analyze data before model development.)

Conduct deep research on asset-related, operational, and domain-specific datasets to understand root causes, trends, and predictive signals.

What youll bring

Required experience

57 years total software/ML engineering experience, with 3+ years building and operating ML systems in production.

1+ years handson LLM application development (e.g., RAG, finetuning, prompt engineering, evaluators/guardrails, agentic workflows) using packages such as Langchain and Langgraph.

AWS proficiency (3+ years): Strong with core services (EKS/ECS, Lambda, S3, DynamoDB/RDS, Step Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS). (Representative AWS ML role skills.)

Modeling frameworks: Python, PyTorch, Hugging Face ecosystem; vector stores (e.g., OpenSearch, PGVector, Pinecone), embeddings, retrieval, and evaluation metrics for NLP/LLMs. (In line with senior LLM roles.)

MLOps: CI/CD for ML, model registries,



experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes, GitHub Actions/GitLab CI. (Current MLOps expectations.)

Data engineering fluency: ETL/ELT, streaming/batch (Spark/Flink), data quality and governance controls for ML.

Nice to have

Experience with distributed training (FSDP, DeepSpeed), RLHF, or Inferentia/Trainium optimization.

Exposure to sustainability/asset/intelligent operations domains.

Familiarity with security compliance for ML systems in enterprise environments. (Frequently included in senior ML roles.)

How youll work

Pragmatic and productoriented: You bias to measurable outcomes and iterate quickly with stakeholders. (Modern senior ML role framing.)

Engineering excellence: You write productionquality Python, design reliable APIs/services, and uphold testing/observability standards. (Common duties in senior templates.)

Collaborative leadership: You mentor peers and influence architecture across teams. (Industrystandard senior expectations.)

Qualifications

Bachelors in CS/EE/Math or related field (Masters preferred) or equivalent practical experience. (Typical for senior ML roles.)

Join a missiondriven team building technology that keeps communities runningsafer, greener, and more resilientat global scale. Youll pair startupspeed product work with the reach and rigor of Siemens.

The Brightly culture

Were guided by a vision of community that serves the ambitions and wellbeing of all people, and our professional communities are no exception. We model that ideal every day by being supportive, cooperative partners to one another, conscientiously making space for our colleagues to grow and thrive. Our passionate team is driven to create a future where smarter infrastructure protects the environments that shape and connect us all. That brighter future starts with us.

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📌 Senior Machine Learning Engineer (Noida)
🏢 Siemens
📍 Noida