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).
• 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.
Apply Now
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