MHTECHIN – Machine Learning Engineer

Job Description

MHTECHIN is looking for a Machine Learning Engineer to join its technology team and work on practical artificial intelligence solutions across fintech, agri-tech, and SME-focused products. This opportunity is suited to professionals who want to take machine learning models beyond experimentation and contribute directly to production applications used by real customers. The Machine Learning Engineer will be responsible for translating business requirements into measurable machine learning problems, preparing and analyzing datasets, developing meaningful features, establishing baseline models, conducting experiments, and selecting appropriate approaches based on performance and business impact. The role requires a strong combination of programming, statistics, machine learning, data engineering, and software development skills. Candidates will work with Python and popular data science libraries such as NumPy, Pandas, and scikit-learn, while also applying deep learning frameworks such as PyTorch or TensorFlow when the problem requires more advanced modeling techniques.

The position covers the complete machine learning lifecycle, from initial data exploration and model experimentation through deployment, monitoring, optimization, and ongoing maintenance. The selected candidate will develop supervised, unsupervised, and deep learning models depending on the business problem and will be expected to evaluate models using appropriate metrics rather than relying solely on training performance. Building reliable ETL and machine learning pipelines will also be an important part of the role, including workflows for data preparation, feature engineering, model training, batch inference, and real-time inference. Candidates should have practical cloud experience with platforms such as AWS, Google Cloud, or Microsoft Azure and understand how Docker, Git, CI/CD, model registries, and monitoring fit into a production-oriented MLOps environment. The role therefore goes beyond traditional data science experimentation and requires engineers who can turn machine learning concepts into maintainable, scalable, and testable software.

As a Machine Learning Engineer at MHTECHIN, collaboration with product and engineering teams will be essential. Models need to become useful product capabilities, which means integrating machine learning services with APIs, applications, data platforms, and operational systems. Depending on the project, engineers may work with technologies such as FastAPI, Flask, Kafka, Pub/Sub, vector databases, Airflow, Prefect, feature stores, or model monitoring platforms. Experience in areas such as natural language processing, transformer models, time-series forecasting, recommendation systems, or modern AI infrastructure can provide an additional advantage. MHTECHIN describes the role as one with significant ownership in a small and fast-moving environment, giving engineers the opportunity to make technical decisions and see their work reach production. For professionals with 2–6 years of experience who want to develop production-grade machine learning systems while gaining exposure to cloud computing, MLOps, data pipelines, and applied AI, this position offers a broad engineering-focused career opportunity.

Roles & Responsibilities

1. Translate Business Problems Into ML Solutions
   Understand product and business requirements and convert them into clearly defined machine learning problems with measurable objectives, appropriate evaluation metrics, and practical success criteria.
2. Explore and Prepare Data
   Analyze raw datasets, identify data quality issues, handle missing or inconsistent information, and prepare reliable datasets suitable for model development and production workflows.
3. Develop Machine Learning Features
   Design and implement meaningful features that improve model performance while maintaining reproducibility, scalability, and consistency between training and production environments.
4. Build and Evaluate Models
   Train supervised, unsupervised, and deep learning models using appropriate algorithms and evaluate their performance using relevant statistical and business metrics.
5. Conduct Reproducible Experiments
   Establish reliable experimentation practices that allow model versions, datasets, configurations, and results to be tracked and compared consistently.
6. Optimize Machine Learning Models
   Tune model parameters, investigate performance bottlenecks, compare alternative approaches, and optimize models for accuracy, efficiency, latency, and production requirements.
7. Build Data and ML Pipelines
   Develop robust ETL and machine learning pipelines supporting data processing, feature generation, model training, batch inference, and real-time prediction workflows.
8. Deploy Production Models
   Package and deploy machine learning models using cloud infrastructure, containers, APIs, and appropriate MLOps practices to make models available to applications and customers.
9. Implement MLOps Practices
   Contribute to CI/CD workflows, model versioning, model registries, automated testing, monitoring, logging, and other practices required for reliable machine learning operations.
10. Integrate Models With Applications
    Work closely with software engineers to expose models through APIs and integrate predictions into customer-facing products, internal applications, and business workflows.
11. Monitor Production Performance
    Track model behavior and system health after deployment, investigate performance degradation or data changes, and make improvements when models no longer meet expected standards.
12. Collaborate Across Teams
    Partner with product managers, data professionals, software engineers, and other stakeholders to ensure machine learning solutions are technically sound, commercially useful, and maintainable.

Requirements & Eligibility

1. Professional ML Experience
   Candidates should have 2–6 years of hands-on experience in machine learning, data science, machine learning engineering, or a closely related technical field, preferably including production deployments.
2. Strong Python Programming
   Excellent Python skills are required, with practical experience writing clean, maintainable, reusable, and tested code for machine learning and data processing applications.
3. Data Science Libraries
   Experience with NumPy, Pandas, and scikit-learn is expected. Candidates should be comfortable using these libraries for data manipulation, analysis, feature engineering, modeling, and evaluation.
4. Deep Learning Knowledge
   Practical experience with at least one major deep learning framework, preferably PyTorch or TensorFlow, is required for projects involving neural networks and advanced machine learning techniques.
5. Cloud Deployment Experience
   Candidates should have hands-on experience deploying applications or machine learning workloads on AWS, Google Cloud Platform, or Microsoft Azure.
6. Docker & CI/CD
   Familiarity with Docker and Git-based CI/CD workflows is important for packaging, testing, deploying, and maintaining machine learning applications consistently across environments.
7. SQL & Data Modeling
   Strong SQL skills are required, along with a practical understanding of relational data, data modeling, joins, aggregations, data quality, and efficient data retrieval.
8. Production Engineering Mindset
   Candidates should understand that production machine learning requires more than model accuracy. Reliability, scalability, testing, observability, security, deployment, and maintainability are equally important.
9. Problem-Solving Ability
   Strong analytical and troubleshooting skills are essential for investigating data issues, model failures, performance problems, pipeline errors, and unexpected production behavior.
10. Educational Background
    A Bachelor’s degree in Computer Science, Artificial Intelligence, Electrical Engineering, or a related technical discipline is preferred. Equivalent practical experience may also be considered.

Nice-to-Have Skills

• Experience with feature stores and feature management workflows.
• Knowledge of Apache Airflow or Prefect for workflow orchestration.
• Experience with model monitoring, model registries, and ML observability.
• Knowledge of Natural Language Processing and transformer-based models.
• Experience developing time-series forecasting or prediction models.
• Exposure to recommendation systems and personalization algorithms.
• Experience building machine learning APIs using FastAPI or Flask.
• Familiarity with Kafka, Google Pub/Sub, or other streaming technologies.
• Experience working with vector databases and modern AI retrieval architectures.
• Understanding of data security, privacy, governance, and compliance requirements.

Expected Salary

The advertised compensation for the MHTECHIN Machine Learning Engineer position is ₹12 lakh to ₹24 lakh per year. This is a competitive range for a 2–6 year machine learning engineering role in Pune, particularly for candidates with production ML, cloud deployment, Python, MLOps, and deep learning experience. Actual compensation may vary based on experience, technical expertise, interview performance, and the overall compensation structure.

Why Consider This Opportunity?

The MHTECHIN Machine Learning Engineer position provides an opportunity to work on production-focused artificial intelligence rather than limiting the role to research, experimentation, or academic model development. Engineers can contribute to solutions spanning fintech, agri-tech, and SME enablement, giving them exposure to different business problems and real-world machine learning applications.

The role also offers broad technical exposure across machine learning, deep learning, cloud computing, MLOps, data engineering, APIs, automation, and AI infrastructure. Because the position covers the full model lifecycle, engineers can gain experience in everything from dataset preparation and feature engineering to deployment, monitoring, troubleshooting, and optimization.

For professionals who enjoy working in smaller, fast-moving teams, the role offers substantial ownership and the opportunity to work closely with product and engineering teams. The hybrid/remote-friendly setup, learning support, modern cloud technologies, performance incentives, health insurance, paid time off, and professional development benefits further add to the overall opportunity.

Benefits

• Competitive annual compensation
• Performance-based bonuses
• Health insurance coverage
• Paid time off
• Learning and development stipend
• Modern hardware
• Remote working support
• Hybrid-friendly work environment
• Exposure to production AI projects
• High ownership and mentorship

🚨 Stop Scrolling – This Could Be Your Shortcut to Interviews

Most candidates apply to 100+ jobs and never hear back.
The real reason? They don’t know where recruiters are actually hiring from.

Our April Hiring PDF includes verified HR emails and hiring details from companies like:

Dentsu, IBM, HCL, PwC, LTIMindtree, Wipro, Cognizant, Deloitte, Capgemini, Amazon, TCS, Infosys, EPAM, EY, NTT Data, Tech Mahindra, Fractal, GlobalLogic, Coforge, UST and many more.

Inside you’ll find:
✔ 300+ Fresher Job Opportunities
✔ 2500+ Verified HR Emails & Contacts
✔ Direct Hiring + Consultancy Openings
✔ IT & Non-IT Roles

🔥 60+ students placed recently using these hiring leads

👉 Grab the April Hiring List Now: April Hiring PDF