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 .

Machine Learning Engineer