Machine Learning Engineer

Are you passionate about building intelligent systems that go beyond experiments and actually impact millions of users? As an AI/ML Engineer with us, you won’t just train models in isolation — you’ll get to design, deploy, and scale AI solutions that solve real-world business problems.

This role is perfect for someone with 2 - 5 years of experience in Machine Learning who wants to deepen their expertise in both model development and production deployment. You’ll work alongside data scientists, engineers, and product innovators to bring advanced AI from idea → prototype → production.

Why this role is exciting?

End-to-End Ownership:

Real-World Impact:

Cutting-Edge Tech:

Scalable Systems:

Growth Path:

Responsibilities

1. Design, build and deploy machine learning models and analytical solutions for business use cases

2. Perform end-to-end ML workflows including data cleaning, pre-processing, feature training, model training, evaluation and deployment

3. Analyze large structured and unstructured datasets to derive actionable insights

4. Optimize model accuracy, scalability and perform through tuning and experimentation

5. Collaborate with data engineers, analysts and software teams to integrate ML/analytics solutions into production system

6. Support monitoring, testing and continuous improvement of ML models and data pipeline

7. Stay updated with the latest trends, frameworks and tools in AI/ML data engineering and analytics

Qualification

Bachelor's/Master in computer science, Data science, Artificial intelligence or related field

2 - 5 Years of hands-on experience in building and deploying ML/analytics models in production or applied business environments

Required Skills

1. Strong proficiency in python and ML/data libraries (Numpy, pandas, sickit-learn, Tensorflow, Matplotlib)

2. Solid understanding of ML algorithm (classification, regression, clustering, recommendation, NLP)

3. Proficiency in SQL and data handling techniques across structured and unstructured data

4. Familiarity with MLOps practices

5. Hands-on experience with Large Language models (LLMs) such as GPT, LLaMA

6. Familiarity with LLM deployment and optimization for inference (e.g. API integration, vector database and RAG pipeline)

7. Strong problem solving, analytical and communication skills to explain technical findings in simple terms 

Good to have skills

1. Experience with vector databases (Pinecone) or document databases (Elasticsearch).

2. Knowledge of basic data visualization tools (Power BI, Tableau, or similar).

3. Familiarity with Flask API for deploying ML models

• 4. Hands-on experience with Docker/Kubernetes for containerized ML/data applications.