Senior Machine Learning Engineer:

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

About Ankercloud:

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

In AI/ML, Ankercloud positions its work around production-grade machine learning, predictive analytics,

computer vision, NLP, MLOps, Generative AI, and Agentic AI, with delivery patterns spanning discovery, MVPs,

proof-of-value programs, and enterprise-scale rollouts.

Role Overview:

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. This role is ideal for someone who can move fluidly from problem framing and experimentation to deployment, observability, optimization, and continuous improvement in production.

The role sits at the intersection of machine learning engineering, applied research, MLOps, and customer delivery. It requires strong technical depth, good product judgment, and the ability to translate ambiguous business problems into reliable, scalable, and measurable AI solutions for global customers.

What You Will Do:

Build Applied AI Solutions

- Own the design and development of ML and GenAI solutions from discovery to production, including data preparation, feature engineering, model selection, evaluation, deployment, and iteration.
- Build solutions across domains such as NLP, OCR, computer vision, forecasting, recommendation systems,

anomaly detection, synthetic data generation, and intelligent automation.

- Develop enterprise-ready applications using modern LLM and GenAI patterns including prompt engineering,

retrieval-augmented generation, embeddings, vector search, tool use, and agentic workflows.

Productionize and Scale

- Design, deploy,



and maintain robust MLOps pipelines that support repeatable experimentation, CI/CD, model versioning, monitoring, and governance across AWS and GCP environments.
- Use cloud-native AI platforms such as Amazon SageMaker, AWS Bedrock, Vertex AI, and related services to train, tune, deploy, and optimize solutions for performance, reliability, and cost.
- Improve real-world model performance through strong validation strategies, A/B testing, observability, drift detection, feedback loops, and systematic error analysis.

Solve Customer Problems

- Partner with Sales/Pre-Sales, product leaders, architects, and data engineers to turn business goals into measurable ML problem statements, delivery plans, and technical solutions.
- Work across multiple industries and use cases, adapting quickly to new data environments, operational constraints, compliance expectations, and decision workflows.
- Communicate clearly with both technical and non-technical stakeholders, 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 help shape standards for model quality, platform reliability, security, and maintainability.
- Stay current with quick-moving advances in LLMs, agent frameworks, 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 machine learning solutions in production environments (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.
- 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 as MLflow, Kubeflow, containerization, branching strategies, and production monitoring.
- Working knowledge of AWS and GCP AI/ML services, especially SageMaker, Bedrock, Vertex AI, AutoML,

BigQuery ML, or closely related managed offerings.

- Strong problem-solving ability, engineering rigor, and a bias toward shipping solutions that are useful,

measurable, and maintainable.

- Excellent communication skills and comfort working directly with 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 or similar orchestration frameworks, and evaluation methods for LLM applications.
- Experience optimizing GPU workloads, scaling inference, or managing cost-performance trade-offs for enterprise AI deployments.
- Background in consulting, customer-facing delivery, or regulated-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 rather than 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, and room to influence how Ankercloud builds, delivers,

and scales applied AI solutions.

📌 Sr ML Engineer (Bengaluru)
🏢 ANKERCLOUD TECHNOLOGIES
📍 Bengaluru