Staff Machine Learning Engineer

Q2; Bengaluru; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Q2 in Bengaluru seeks an accomplished Machine Learning Engineer (Advanced) to lead end-to-end AI initiatives—from problem framing to deployment and continuous improvement. You will architect scalable ML/AI systems, develop GenAI solutions, and drive adoption across product and engineering teams.

The role demands 8+ years of hands-on ML experience, strong statistics, and cloud proficiency. You will mentor engineers and ensure production readiness of AI capabilities within enterprise applications.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field.
• 8+ years of experience in machine learning, AI, or data-driven system development.
• Proven track record of building and deploying production-grade ML/AI systems at scale.
• Strong foundation in statistics, optimization, probability, and experimental design.
• Expertise in Python and ML ecosystems (PyTorch, TensorFlow, scikit-learn).
• Hands-on experience with Generative AI / LLMs, including Retrieval-Augmented Generation (RAG).
• Experience designing end-to-end ML pipelines and MLOps workflows, including CI/CD for ML, model monitoring, drift detection, and experiment tracking.
• Experience with cloud platforms (AWS, GCP, Azure) and scalable data/compute systems.

Responsibilities

• Own end-to-end AI solution lifecycle from problem definition, experimentation, and model development to production deployment, adoption, and impact measurement.
• Design and architect scalable ML/AI systems, including data pipelines, model training, evaluation, serving, monitoring, and retraining.
• Build and deploy GenAI and agentic AI solutions, including LLM-based systems, RAG pipelines, and multi-agent workflows integrated into enterprise applications.
• Drive measurable business outcomes, including efficiency gains, cost reduction, quality improvements, and cycle-time reduction.
• Collaborate cross-functionally with Product, Engineering, and Delivery teams to identify high-impact use cases and ensure successful integration into workflows.
• Establish best practices and reusable frameworks for ML, GenAI, and agentic AI development across teams.
• Lead technical initiatives and mentor engineers, elevating team capability in AI/ML system design and implementation.
• Ensure production readiness and reliability through robust testing, validation, monitoring, and governance of AI systems.
• Stay current with emerging AI/ML advancements and evaluate their applicability to business problems.

Skills

8+ years experience in ML/AI systems

Production-grade ML/AI systems atscale

Statistics/Optimization/Probability/Ex

Python and ML ecosystems (PyTorch,TF,‑

Generative AI/LLMs (RAG)

ML pipelines & MLOps (CI/CD,Monitoring

Cloud platforms (AWS,GCP,Azure)

Education

Bachelor’s or Master’s in CS/Engineering/Math

Tools

PyTorch

TensorFlow

scikit-learn

LangChain

Semantic Kernel

Spark

Ray

AWS

GCP

Azure

Job description

The Machine Learning Engineer (Advanced) is a technical leader responsible for designing, building, and scaling production‑grade AI systems that drive measurable business impact. This role goes beyond model development to owning end‑to‑end AI solutions—from problem definition through deployment, adoption, and continuous improvement.

Operating within the AI Producer model, this role partners with Product, Engineering, and Delivery teams to translate high‑value opportunities into scalable AI capabilities, including agentic workflows and GenAI‑powered systems.

KEY RESPONSIBILITIES

• Own end‑to‑end AI solution lifecycle from problem definition, experimentation, and model development to production deployment, adoption, and impact measurement
• Design and architect scalable ML/AI systems, including data pipelines, model training, evaluation, serving, monitoring, and retraining
• Build and deploy GenAI and agentic AI solutions, including LLM‑based systems, RAG pipelines, and multi‑agent workflows integrated into enterprise applications
• Drive measurable business outcomes, including efficiency gains, cost reduction, quality improvements, and cycle‑time reduction
• Collaborate cross‑functionally with Product, Engineering, and Delivery teams to identify high‑impact use cases and ensure successful integration into workflows
• Establish best practices and reusable frameworks for ML, GenAI, and agentic AI development across teams
• Lead technical initiatives and mentor engineers, elevating team capability in AI/ML system design and implementation
• Ensure production readiness and reliability through robust testing, validation, monitoring, and governance of AI systems
• Stay current with emerging AI/ML advancements and evaluate their applicability to business problems

EXPERIENCE AND QUALIFICATIONS

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field
• 8+ years of experience in machine learning, AI, or data‑driven system development
• Proven track record of building and deploying production‑grade ML/AI systems at scale
• Strong foundation in statistics, optimization, probability, and experimental design
• Expertise in Python and ML ecosystems (PyTorch, TensorFlow, scikit‑learn)
• Hands‑on experience with Generative AI / LLMs, including:
• Retrieval‑Augmented Generation (RAG)
• Evaluation and deployment of LLM‑based systems
• Experience designing end‑to‑end ML pipelines and MLOps workflows, including:
• CI/CD for ML
• Model monitoring and drift detection
• Experiment tracking and versioning
• Experience with cloud platforms (AWS, GCP, Azure) and scalable data/compute systems

PREFERRED SKILLS

• Advanced expertise in deep learning architectures (transformers, sequence models, etc.)
• Experience with agentic AI architectures and orchestration frameworks (e.g., LangChain, Semantic Kernel)
• Familiarity with vector databases, embeddings, and retrieval systems
• Experience with distributed data processing frameworks (e.g., Spark, Ray)
• Understanding of secure, scalable, and governed AI system design (RBAC, data privacy, model governance)
• Experience working in cross‑functional environments driving AI adoption in business workflows

RELEVANT EXPERIENCE AND IMPACT

• Delivered AI/ML solutions that were successfully deployed and adopted in production workflows
• Demonstrated measurable impact through reduction in manual effort, operational cost, or cycle time
• Built scalable, reusable AI components or frameworks leveraged across teams
• Partnered effectively across Product, Engineering, and Delivery to accelerate solution development and adoption