AI ML Developer

Tims Learning Solutions; Bengaluru; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Tims Learning Solutions is seeking an experienced AI/ML Developer in Bengaluru, India, to design and deploy intelligent systems that leverage both traditional and advanced AI capabilities. The role demands a sound knowledge of machine learning and deep learning, along with practical experience in developing scalable AI applications.

The ideal candidate possesses strong technical leadership skills and will work closely with data engineering teams on complex datasets. A background in cloud AI ecosystems is essential.

Qualifications

• 5+ years of experience developing and shipping machine learning systems.
• Proficiency in containerization and model deployment.
• Experience in mentoring junior engineers.

Responsibilities

• Design and fine-tune machine learning models for business solutions.
• Architect end-to-end data and machine learning pipelines.
• Collaborate with data engineers for exploratory data analysis.

Skills

Python

Deep Learning

Machine Learning Theory

PyTorch

TensorFlow

MLOps

Docker

Kubernetes

Cloud AI Ecosystems

Education

Bachelor's degree in Computer Science

Master's degree in Data Science

Tools

MLflow

Weights & Biases

Pandas

NumPy

Hugging Face

Scikit-learn

Job description

We are seeking an experienced and visionary AI/ML Developer to design, develop, and deploy intelligent systems across our product ecosystem. In this role, they will bridge the gap between complex mathematical research and scalable software engineering. They will be responsible for everything from building traditional predictive models to deploying advanced Generative AI architectures, ensuring our systems are highly performant, robust, and cost-effective.

Role & responsibilities

1. Model Architecture & Development: Design, train, and fine-tune machine learning, deep learning, and large-scale Generative AI models (including LLMs, computer vision, or multi-modal systems) to solve critical business problems.
2. Generative AI Innovation: Build and optimize advanced AI capabilities using Retrieval-Augmented Generation (RAG), vector databases, custom prompt engineering, and agentic workflows.
3. Production Pipeline & MLOps: Architect end-to-end data and machine learning pipelines. Containerize and deploy models into production environments with continuous monitoring for concept drift and performance degradation.
4. Data Strategy: Collaborate with data engineering teams to clean, restructure, and perform exploratory data analysis (EDA) on high-dimensional, complex datasets.
5. Technical Leadership: Act as a subject matter expert, mentoring junior engineers, establishing coding standards, and defining the company's broader AI roadmap.

Required Technical Skills & Qualifications

Core Machine Learning & Languages

• Languages: Expert-level proficiency in Python (C++ or Java is a plus).
• Frameworks: Extensive hands-on experience with PyTorch or TensorFlow, along with Scikit-learn, Hugging Face, and NumPy/Pandas.
• Mathematics: Deep, fundamental understanding of linear algebra, calculus, statistics, and machine learning theory.

Advanced AI & GenAI

• LLMs & Frameworks: Experience fine-tuning open-source models (e.g., Llama, Mistral) using optimization techniques like LoRA/QLoRA.
• Experience with PDF/document processing libraries such as PyMuPDF, pypdf, and pdfplumber
• Orchestration: Experience using LangChain, LlamaIndex, or AutoGen to construct multi-agent AI features.
• Vector Search: Practical knowledge of vector databases such as Pinecone, Milvus, or Chroma for semantic search.

Infrastructure & MLOps

• Cloud Platforms: Proven experience working with cloud AI ecosystems (AWS SageMaker, Google Vertex AI, or Azure AI).
• DevOps/MLOps: Proficiency with Docker, Kubernetes, and lifecycle tools like MLflow or Weights & Biases for experiment tracking.

Experience & Soft Skills

• Experience: 5+ years of dedicated professional experience developing and shipping ML systems, with at least 1-2 years focused on Generative AI applications.
• Education: Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related quantitative field (Ph.D. preferred for research-heavy roles).
• Communication: Strong ability to explain complex statistical or AI architectural decisions to non-technical business leaders and stakeholders.