Sr ML Engineer

Ankercloud; Bengaluru Urban; On-site; INR 2,000,000 - 3,600,000; Full time

Job summary

Ankercloud is hiring a Senior Machine Learning Engineer to design, build, and productionize AI systems that solve high-value customer problems across cloud-native environments. You will work across ML engineering, applied research, MLOps, and customer delivery to translate business problems into reliable AI solutions.

The role requires hands-on production ML experience, strong Python and ML framework skills, and deep knowledge of AWS/GCP services.

Qualifications

• 5+ years of hands-on experience building and deploying machine learning solutions in production (AWS or Google Cloud experience is a must).
• Strong proficiency in Python and common ML/DL frameworks such as PyTorch, TensorFlow, Keras, and ecosystem tooling for experimentation and deployment.
• Experience with NLP, computer vision, OCR, recommender systems, or Generative AI / LLM applications in real-world settings.

Responsibilities

• Build applied AI solutions from discovery to production, including data prep, feature engineering, model selection, evaluation, deployment, and iteration.
• Develop enterprise-ready applications using modern GenAI patterns including prompt engineering, embeddings, vector search, tool use, and agentic workflows.
• Design and maintain robust MLOps pipelines for repeatable experimentation, CI/CD, model versioning, monitoring, and governance across AWS and GCP.

Skills

Python

PyTorch

TensorFlow

Keras

NLP

Computer Vision

OCR

Generative AI

LLM apps

MLOps

AWS/GCP AI services

Tools

MLflow

Kubeflow

Docker

CI/CD pipelines

Job description

Bangalore North, India | Posted on 06/10/2026

Ankercloud is a cloud consulting company that specializes in helping businesses maximize their use of public cloud platforms like AWS and Google Cloud. Our services cover a wide range of cloud solutions, including cloud operations (CloudOps), cost optimization, security, and compliance. We offer AI/ML solutions, with a focus on generative AI, predictive maintenance, and demand forecasting. Additionally, Ankercloud supports the transformation of traditional software into SaaS-based solutions, making it easier for companies to scale and innovate using cloud infrastructure.

We are scaled up from a Small to Medium phase business, We are currently expanding across several locations Globally and one of our recent launches is in the USA.

We cater to small, medium, and large enterprise businesses. Some of our clients include those in manufacturing, fin-tech, retail, healthcare and ed-tech.

We specialize in offering AWS solutions in Big data, Cloud and ML and Artificial intelligence. We have partnered with AWS, Google Cloud and Microsoft Azure.

Job Description

Senior Machine Learning Engineer

Location: Bengaluru (HBR Layout, Kalyan Nagar) / Pune | Mode: 5Days Work from Office | Type: Full-time

About Ankercloud

Ankercloud is a global technology consulting and implementationpartner that helps ambitious companies turn bold ideas into real products using cloud, data, AI/ML, andsecurity. Ankercloud is a Premier Tier Partner for both AWS and Google Cloud, with teams serving customers acrossregions and industries.

In AI/ML, Ankercloud positions its work around production-grademachine learning, predictive analytics, computer vision, NLP, MLOps, Generative AI, and Agentic AI, withdelivery patterns spanning discovery, MVPs, proof-of-value programs, and enterprise-scale rollouts.

Role Overview

Ankercloud is hiring a Senior Machine Learning Engineer todesign, build, and productionize AI systems that solve high-value customer problems across cloud-nativeenvironments. This role is ideal for someone who can move fluidly from problem framing and experimentation todeployment, observability, optimization, and continuous improvement in production.

The role sits at the intersection of machine learningengineering, applied research, MLOps, and customer delivery. It requires strong technical depth, good productjudgment, and the ability to translate ambiguous business problems into reliable, scalable, and measurable AIsolutions for global customers.

What You Will Do

Build Applied AI Solutions

• Own the design and development of ML and GenAI solutions fromdiscovery to production, including data preparation, feature engineering, model selection, evaluation,deployment, and iteration.
• Build solutions across domains such as NLP, OCR, computervision, forecasting, recommendation systems, anomaly detection, synthetic data generation, and intelligent automation.
• Develop enterprise-ready applications using modern LLM andGenAI patterns including prompt engineering, retrieval-augmented generation, embeddings, vector search, tooluse, and agentic workflows.
• Design, deploy, and maintain robust MLOps pipelines thatsupport repeatable experimentation, CI/CD, model versioning, monitoring, and governance across AWS and GCP environments.
• Use cloud-native AI platforms such as Amazon SageMaker, AWSBedrock, Vertex AI, and related services to train, tune, deploy, and optimize solutions for performance,reliability, and cost.
• Improve real-world model performance through strong validationstrategies, A/B testing, observability, drift detection, feedback loops, and systematic error analysis.
• Partner with Sales/Pre-Sales, product leaders, architects, anddata engineers to turn business goals into measurable ML problem statements, delivery plans, and technical solutions.
• Work across multiple industries and use cases, adapting quicklyto new data environments, operational constraints, compliance expectations, and decision workflows.
• Communicate clearly with both technical and non-technicalstakeholders, helping customers understand trade-offs, timelines, model behavior, and expected business impact.

Raise the Bar

• Contribute reusable accelerators, reference architectures, evaluation templates, and engineering best practices that improve delivery speed and quality across the AIML organization.
• Mentor engineers, review technical designs and code, and helpshape standards for model quality, platform reliability, security, and maintainability.
• Stay current with fast-moving advances in LLMs, agentframeworks, cloud AI services, and applied ML tooling, and bring the best ideas into real customer delivery.

Who You Are

• 5+ years of hands-on experience building and deploying machinelearning solutions in production environments (AWS or Google Cloud experience is a must).
• Strong proficiency in Python and common ML/DL frameworks suchas PyTorch, TensorFlow, Keras, and ecosystem tooling for experimentation and deployment.
• Solid experience with supervised and unsupervised learning,deep learning, model evaluation, feature engineering, and statistical reasoning.
• Experience with NLP, computer vision, OCR, recommender systems,or Generative AI / LLM applications in real-world settings.
• Practical exposure to MLOps platforms and workflows such asMLflow, Kubeflow, containerization, branching strategies, and production monitoring.
• Working knowledge of AWS and GCP AI/ML services, especiallySageMaker, Bedrock, Vertex AI, AutoML, BigQuery ML, or closely related managed offerings.
• Strong problem-solving ability, engineering rigor, and a biastoward shipping solutions that are useful, measurable, and maintainable.
• Excellent communication skills and comfort working directlywith distributed teams and global customers.
• Experience with agentic AI systems, Model Context Protocol(MCP), function/tool calling, or multi-agent workflow orchestration.

Nice to Have

• Familiarity with vector databases, embeddings, LangChain orsimilar orchestration frameworks, and evaluation methods for LLM applications.
• Experience optimizing GPU workloads, scaling inference, ormanaging cost-performance trade-offs for
• Background in consulting, customer-facing delivery, orregulated-industry use cases such as manufacturing, healthcare, financial services, or mobility.

What Should Excite You

• The chance to work on a wide portfolio of AI problems ratherthan one narrow internal use case, across industries and solution types.
• Exposure to modern AWS and Google Cloud AI ecosystems,including enterprise GenAI and agentic architectures deployed in real customer environments.
• A role with visible ownership, strong learning velocity, androom to influence how Ankercloud builds, delivers,