GenAI/ML Engineer

GenAI/ML Engineer

Key Responsibilities

• Design, develop

  , and deploy

  AI/ML models

  and

  Generative AI

  applications for various enterprise use cases.
• Implement and integrate

  Large Language Models (LLMs)

  using frameworks such as

  LangChain

  ,

  LlamaIndex

  , and

  RAG pipelines

  .
• Develop

  Agentic AI systems

  capable of

  multi-step reasoning

  and

  autonomous decision-making

  .
• Create secure and scalable

  data ingestion pipelines

  for structured and unstructured data, enabling indexing, vector search, and advanced retrieval techniques.
• Collaborate with

  cross-functional teams

  (Data Engineers, Product Managers, Architects) to deploy AI solutions and enhance the AI stack.
• Build

  CI/CD pipelines

  for

  ML/GenAI workflows

  and support

  end-to-end MLOps

  practices.
• Leverage

  Azure

  and

  Databricks

  for

  training

  ,

  serving

  , and

  monitoring AI models

  at scale.

Required Qualifications & Skills

• 4+ years

  of hands-on experience in

  AI/ML development

  , including

  Generative AI applications

  .
• Expertise in

  RAG

  ,

  LLMs

  , and

  Agentic AI

  implementations.
• Strong experience with

  LangChain

  ,

  LlamaIndex

  , or similar LLM orchestration frameworks.
• Proficiency in

  Python

  and key

  ML/DL libraries

  :

  TensorFlow

  ,

  PyTorch

  ,

  Scikit-learn

  .
• Solid foundation in

  Deep Learning

  ,

  Natural Language Processing (NLP)

  , and

  Transformer-based architectures

  .
• Experience in building

  data ingestion

  ,

  indexing

  , and

  retrieval pipelines

  for real-world enterprise use cases.
• Hands-on experience with

  Azure

  cloud services and

  Databricks

  .
• Proven track record in designing

  CI/CD pipelines

  and using MLOps tools like

  MLflow

  ,

  DVC

  , or

  Kubeflow

  .

Soft Skills

• Strong

  problem-solving

  and

  critical thinking

  ability.
• Excellent

  communication skills

  , with the ability to explain complex AI concepts to non-technical stakeholders.
• Ability to

  collaborate effectively

  in

  agile

  ,

  cross-functional teams

  .
• A

  growth mindset

  , eager to explore and learn emerging technologies.

Preferred Qualifications

• Familiarity with

  vector databases

  such as

  FAISS

  ,

  Pinecone

  , or

  Weaviate

  .
• Experience with

  AutoGPT

  ,

  CrewAI

  , or similar

  agent frameworks

  .
• Exposure to

  Azure OpenAI

  ,

  Cognitive Search

  , or

  Databricks ML tools

  .
• Understanding of

  AI security

  ,

  responsible AI

  , and

  model governance

  .

Role Dimensions

• Design and implement innovative

  GenAI applications

  to address complex business problems.
• Work on large-scale, complex AI solutions in collaboration with cross-functional teams.
• Take ownership of the

  end-to-end AI pipeline

  , from model development to deployment and monitoring.

Success Measures (KPIs)

• Successful

  deployment

  of AI and

  Generative AI applications

  .

• Optimization

  of data pipelines and model performance at scale.
• Contribution to the

  successful adoption

  of AI-driven solutions within enterprise use cases.
• Effective

  collaboration

  with cross-functional teams, ensuring smooth deployment of AI workflows.

Competency Alignment

• AI/ML Development

  : Expertise in building and deploying scalable and efficient AI models.

• Generative AI

  : Strong hands-on experience in

  Generative AI

  ,

  LLMs

  , and

  RAG

  frameworks.

• MLOps

  : Proficiency in designing and maintaining

  CI/CD pipelines

  and implementing

  MLOps practices

  .

• Cloud Platforms

  : Experience with

  Azure

  and

  Databricks

  for AI model training and serving.