Sr ML Engineer (Bengaluru)

Sr ML Engineer (Bengaluru)

13 Aug
|
Nameless
|
Bengaluru

13 Aug

Nameless

Bengaluru

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 fast -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).






• Robust 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)
🏢 Nameless
📍 Bengaluru

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