- 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 .
