AI ML Lead

Job Position:- AIML Lead

Experience Required- 7-10 years

Job Type- Full-time

Location - Hyderabad, India

Notice:- 0-30days

Overview

dynamic AI/ML Leader

building high-performing teams

Key Responsibilities

1. AI/ML Solution Development & Delivery

• Lead the design, development, and deployment of

  AI and ML-based solutions

  aligned with business objectives.
• Evaluate and implement

  AI frameworks, libraries, and platforms

  (TensorFlow, PyTorch, Hugging Face, LangChain, AutoML).
• Define and execute

  data strategies

  for AI/ML pipelines including data ingestion, feature engineering, and model lifecycle management.
• Ensure that AI models are

  scalable, maintainable, and production-ready

  for real-time and batch applications.

2. Cloud & Infrastructure Enablement

• Oversee the deployment of AI workloads on

  AWS, GCP, Azure, or OCI

  with a focus on performance, scalability, and cost-efficiency.
• Lead the design of

  containerized and serverless AI solutions

  using Docker, Kubernetes, and modern DevOps practices.
• Ensure

  security, reliability, and fault tolerance

  across AI infrastructure.

3. Data Strategy & Management

• Collaborate with data engineering teams to build

  robust data architectures

  supporting AI-driven analytics and automation.
• Establish

  data governance frameworks

  , storage strategies, and metadata management processes.
• Leverage big data platforms such as

  Databricks, Snowflake, and Spark

  for model training and data processing.

4. MLOps & Continuous Delivery

• Build and manage

  end-to-end MLOps pipelines

  using MLflow, Kubeflow, SageMaker, or Vertex AI.
• Integrate

  CI/CD processes

  for model versioning, validation, and deployment automation.
• Implement

  monitoring and alerting mechanisms

  to ensure model performance and reliability in production.

5. Leadership & Collaboration

• Lead a cross-functional team of

  AI engineers, data scientists, and product owners

  to deliver impactful AI initiatives.
• Provide

  mentorship and technical guidance

  to team members and foster a culture of innovation.
• Collaborate with product and business stakeholders to align AI initiatives with strategic priorities.
• Conduct technical reviews and enforce best practices across teams.

6. Performance Optimization

• Monitor AI model performance and drive

  optimization for accuracy, latency, and resource utilization

  .
• Develop and implement

  cost management and scaling strategies

  for AI workloads.
• Troubleshoot and resolve complex system and deployment challenges.

7. Innovation & Research

• Stay abreast of

  emerging AI trends and technologies

  , including Generative AI and agentic automation.
• Drive

  proof-of-concept (PoC)

  projects to evaluate new AI capabilities.
• Contribute to

  research publications, patents, and AI community engagements

  to promote organizational thought leadership.

8. Stakeholder Engagement

• Present AI/ML strategies and outcomes to leadership and technical stakeholders.
• Support

  business development and pre-sales efforts

  through technical presentations and solution demonstrations.
• Collaborate with customers and partners to define solution roadmaps and drive successful delivery.

Experience Requirements

• 7+ years

  of total professional experience in

  technology and solution delivery

  .

• 2+ years

  of hands-on experience in

  AI/ML model development, deployment, and scaling

  .

• 5+ years

  of experience with

  cloud platforms

  such as AWS, GCP, Azure, or OCI.

Technical Skills

• AI/ML Frameworks:

  TensorFlow, PyTorch, Hugging Face, LangChain, AutoML.

• Cloud AI Services:

  SageMaker, Vertex AI, Azure ML, Bedrock, LLM APIs.

• MLOps Tools:

  MLflow, Kubeflow, TFX, CI/CD pipelines, Docker, Kubernetes.

• Data Engineering:

  Spark, Databricks, BigQuery, Snowflake, Airflow.

• Programming:

  Python (advanced), Java/Scala, SQL, PySpark.

• Agentic AI:

  RPA tools (UiPath, Automation Anywhere), process mining.
• Proven experience with

  DevOps, automation, and AI lifecycle management

  .

Preferred Qualifications

• Master’s degree in

  Computer Science, Engineering, or related technical field

  .
• Certifications in

  AWS, Azure, or GCP

  (e.g., Solutions Architect or Cloud AI Engineer).
• Experience with

  real-time AI applications

  ,

  streaming data

  , and

  microservices architecture

  .
• Knowledge of

  Responsible AI practices

  , including ethics, fairness, and bias detection.
• Expertise in

  Python, TensorFlow, PyTorch, scikit-learn

  , and other modern AI libraries.

Soft Skills

• Strong

  leadership and mentoring

  capabilities with experience leading technical teams.
• Excellent

  communication and stakeholder management

  skills.
• Strategic and

  analytical problem-solving mindset

  .
• Collaborative, adaptable, and able to thrive in

  fast-paced, evolving environments

  .