4Bell Technology

Job Description

You will work on a broad range of cutting-edge data science and machine learning problems across a variety of industries. You will be engaging with clients to understand their business context. If you are passionate to work on complex unstructured business problems that can be solved using data science and machine learning we would like to talk to you. 

Function: Data Science and Analytics 

Responsibilities: 

• Architect, develop, and deploy machine learning models and AI solutions into live production settings.
• Partner with data scientists to convert prototypes into scalable, enterprise-ready applications.
• Design and implement enterpriseAI systems leveraging LLMs, retrieval-augmented generation (RAG), knowledge graphs, and multi-agent orchestration..
• Embed AI models into existing products, APIs, and broader enterprise systems seamlessly.
• Perform rigorous experimentation, fine-tuning, and error analysis to boost model accuracy and reliability.
• Continuously monitor, maintain, and retrain deployed models to sustain optimal performance.
• Keep abreast of the latest developments in AI/ML, MLOps practices, and cloud-native AI technologies.
• Collaborate with stakeholders to translate business challenges into effective AI-driven solutions.
• Uphold ethical standards and responsible AI principles throughout the AI lifecycle.

Desired Skills & Competencies: 

• Strong learning acumen
• High sense of ownership
• Ability to work in a fast-paced and deadline-driven environment
• Loves technology
• Highly skilled at Data Interpretation
• Problem solver

• Strong programming skills in Python (TensorFlow, PyTorch, scikit-learn) and GenAI frameworks (LangGraph, AutoGen, OpenAI SDK, etc) and prompt engineering on foundation models (OpenAI, HuggingFace, Anthropic, etc.)
• Knowledge of NLP, computer vision, or generative AI (LLMs, diffusion models, RAG, fine tuning models, conversational AI, knowledge graphs etc
• Familiarity with cloud AI services (AWS SageMaker, Azure ML, GCP Vertex AI).
• Solid understanding of data structures, algorithms, and software engineering principles.
• Hands-on experience with model deployment (APIs, microservices, real-time inference).

Nice-to-Have: 

• Experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
• Experience with big data technologies (Spark, Hadoop, Databricks).
• Exposure to responsible AI frameworks and bias detection methods.
• Contributions to open-source AI/ML projects.

Desired Experience & Education: 

• 3 - 5 years of relevant Machine Learning experience.
• B. Tech from Tier-1 college / M.S or M. Tech is preferred in Computer Science, Information Technology, or similar degree.