Machine Learning Engineer

5 days ago

Bengaluru Gurugram Pune, India Anlage Infotech Full time ₹ 15,00,000 - ₹ 20,00,000 per year

Role: GEN AI and Machine Learning Engineer

Exp: 3 - 5 years

5 to 7 Years

7 to 8 Years

Location: Gurgaon and Bengaluru and Pune (hybrid setup 3 days work from office)

Immediate Joiners Only

JD:


• Bachelor's or masters degree in Computer Science, Data Science, Engineering, or a related field.

• Experience on Agentic AI/ Frameworks

• Strong programming skills in languages such as Python, SQL/NoSQL etc.

• Build analytical approach based on business requirements, then develop, train, and deploy machine learning models and AI algorithms

• Exposure to GEN AI models such as OpenAI, Google Gemini, Runway ML etc.

• Experience in developing and deploying AI/ML and deep learning solutions with libraries and frameworks, such as TensorFlow, PyTorch, Scikit-learn, OpenCV and/or Keras.

• Knowledge of math, probability, and statistics.

• Familiarity with a variety of Machine Learning, NLP, and deep learning algorithms.

• Exposure in developing API using Flask/Django.

• Good experience in cloud infrastructure such as AWS, Azure or GCP

• Exposure to Gen AI, Vector DB/Embeddings, LLM (Large language Model)

Skills (good to have)

Experience with MLOps: MLFlow, Kubeflow, CI/CD Pipeline etc.

Good to have experience in Docker, Kubernetes etc

Exposure in HTML, CSS, Javascript/JQuery, , Angular/React

Experience in Flask/Django is a bonus

Responsibilities

• Collaborate with software engineers, business stake holders and/or domain experts to translate business requirements into product features, tools, projects, AI/ML, NLP/NLU and deep learning solutions.

• Develop, implement, and deploy AI/ML solutions.

• Preprocess and analyze large datasets to identify patterns, trends, and insights.

• Evaluate, validate, and optimize AI/ML models to ensure their accuracy, efficiency, and generalizability.

• Deploy applications and AI/ML model into cloud environment such as AWS/Azure/GCP etc.

• Monitor and maintain the performance of AI/ML models in production environments, identifying opportunities for improvement and updating models as needed.

• Document AI/ML model development processes, results, and lessons learned to facilitate knowledge sharing and continuous improvement.

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