MACHINE LEARNING ENGINEER

ZoftSolutions Pvt Ltd; Ernakulam; Remote; INR 1,000,000 - 1,500,000; Full time

Job summary

ZoftSolutions Pvt Ltd is seeking a Machine Learning Engineer to design, develop, and deploy ML models for Agentic AI use cases. You will work with AWS ML services, fine-tune LLMs, and build scalable pipelines in a remote setting.

Ideal candidates have 3–6 years of ML engineering experience, strong Python skills, and hands-on experience with ML lifecycle, data preprocessing, and API deployment. Remote work is supported, with collaboration across cross-functional teams.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field.
• 3–6 years of professional experience in Machine Learning Engineering, AI Engineering, or a related role.
• Proven experience building, deploying, and maintaining scalable ML solutions in production environments.
• Experience working in cloud-native environments, preferably on AWS.

Responsibilities

• Design, develop, and deploy ML models for Agentic AI use cases.
• Preprocess and engineer features from structured, unstructured, and streaming data.
• Collaborate with data engineers to ensure high-quality training datasets.
• Implement LLM fine-tuning, embeddings, and RAG pipelines.
• Evaluate and optimize models for accuracy, performance, and cost-efficiency.
• Integrate models into production applications and APIs.

Skills

Python

ML lifecycle

AWS ML services

LLM / NLP

REST APIs

ML pipeline orchestration

SQL / NoSQL

Education

Bachelor's or Master's in CS/AI/DS/ML

Job description

Nothing can limit your
dreams when you are at
Zoft

MACHINE LEARNING ENGINEER

Key Responsibilities

• Design, develop, and deploy ML models for Agentic AI use cases.
• Work with AWS AI/ML ecosystem (SageMaker, Bedrock, Lambda, Step Functions, S3, DynamoDB, Kinesis).
• Preprocess and engineer features from structured, unstructured, and streaming data.
• Collaborate with data engineers to ensure high-quality, well-curated training datasets.
• Implement LLM fine-tuning, embeddings, and retrieval-augmented generation (RAG) pipelines.
• Evaluate and optimize models for accuracy, performance, scalability, and cost-efficiency.
• Integrate models into production applications and APIs.
• Work with MLOps teams to automate training, testing, deployment, and monitoring workflows.
• Perform experimentation, A/B testing, and model validation to ensure reliability.
• Document experiments, pipelines, and best practices for reproducibility.

Required Skills

• 3–6 years of experience in ML engineering (adjust based on seniority).
• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
• Solid understanding of ML lifecycle (data preprocessing, training, evaluation, deployment).
• Experience with AWS services for ML (SageMaker, Lambda, ECS/EKS, Step Functions, Bedrock).
• Familiarity with large language models (LLMs), NLP, and embeddings.
• Strong knowledge of APIs and microservice deployment.
• Experience with ML pipeline orchestration (Airflow, Kubeflow, MLflow, or similar).
• Understanding of data versioning, experiment tracking, and model registry.
• Proficiency in SQL/NoSQL databases and vector databases (Weaviate, Pinecone, FAISS).

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field.
• 3–6 years of professional experience in Machine Learning Engineering, AI Engineering, or a related role.
• Proven experience building, deploying, and maintaining scalable ML solutions in production environments.
• Experience working in cloud-native environments, preferably on AWS.
• Strong problem-solving, analytical, and debugging skills.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.
• Ability to manage multiple priorities and deliver high-quality solutions within deadlines.

Total Number of Openings : 4

Responsibilities

Design, develop, and deploy ML models for Agentic AI use cases.

Skills

Python

Machine Learning lifecycle

AWS Machine Learning and Cloud Services

LLM, NLP & Generative AI Applications

REST APIs

ML pipeline orchestration tools

SQL and NoSQL databases

Data Versioning, Experiment Tracking, and Model Registry

Work Experience

3-6

Education

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field.

Work Mode

Remote

Creating Ideas with Passion,Purpose, and Technology

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