Machine Learning Engineer

• Define and implement

  model validation processes

  and business success criteria in

  data science terms

  .
• Contribute to the

  architecture and data flow

  for machine learning models.
• Rapidly develop and iterate

  minimum viable solutions (MVS)

  that address enterprise needs.
• Conduct

  advanced data analysis

  and rigorous testing to enhance model accuracy and performance.
• Work with

  Data Architects

  to leverage existing data models and create new ones as required.
• Collaborate with

  product teams and business partners

  to industrialize machine learning models into

  Ericsson s enterprise solutions

  .
• Build

  MLOps pipelines

  for continuous integration, continuous delivery, validation, and monitoring of AI/ML models.
• Design and implement

  effective big data storage and retrieval strategies

  (indexing, partitioning, etc.).
• Develop and maintain

  APIs for AI/ML models

  and optimize data pipelines.
• Lead

  end-to-end ML projects

  from conception to deployment.
• Stay updated on the latest ML advancements and apply best practices to

  enterprise AI solutions

  .

Required Skills Experience

• 4-6 years

  of hands-on experience in

  machine learning, AI, and data science

  .
• Strong knowledge of

  ML frameworks

  (Keras, TensorFlow, Spark ML, etc.).
• Proficiency in

  ML algorithms, deep learning, reinforcement learning (RL), and large language models (LLMs)

  .
• Expertise in

  MLOps

  , including model lifecycle management and monitoring.
• Experience with

  containerization orchestration

  (Docker, Kubernetes, Helm charts).
• Hands-on expertise with

  workflow orchestration tools

  (Kubeflow, Airflow, Argo).
• Strong programming skills in

  Python

  and experience with

  C++, Scala, Java, R

  .
• Experience in

  API design development for AI/ML models

  .
• Hands-on knowledge of

  Terraform

  for infrastructure automation.
• Familiarity with

  AWS services

  (Data Lake, Athena, SageMaker, OpenSearch, DynamoDB, Redshift).
• Strong understanding of

  self-hosted deployment of LLMs on AWS

  .
• Experience in

  RASA, LangChain, LangGraph, LlamaIndex, Django, Open Policy Agent

  .
• Working knowledge of

  vector databases, knowledge graphs, retrieval-augmented generation (RAG), agents, and agentic mesh architectures

  .
• Expertise in

  monitoring tools

  like

  Datadog

  for K8S environments.
• Ability to

  document, present

  , and communicate technical findings to

  business stakeholders

  .
• Proven ability to contribute to

  ML forums, patents, and research publications

  .

Educational Qualifications

• B.Tech/B.E. in Computer Science

  , MCA, or a

  Master s in Mathematics/Statistics

  from a

  top-tier institute

  .