Machine Learning Engineer
Sky Systems, Inc. (SkySys)
Pune City, Maharashtra, India
Role: Associate AI/ML Engineer
Position Type: Full-Time Contract (40hrs/week)
Contract Duration: 6-12 months+
Work Schedule: 8 hours/day (Mon-Fri)
Location: Hybrid 2x/week - Pune, India
We are seeking an Associate AI/ML Engineer with foundational knowledge of machine learning, Python, and SQL. The ideal candidate will support data preparation, model development, evaluation, testing, and integration under the guidance of senior engineers. This is an early-career opportunity for someone who learns quickly, communicates clearly, accepts feedback, and can translate requirements into reliable technical deliverables.
Key Responsibilities
- Prepare, clean, transform, and validate datasets for ML experiments.
- Develop and evaluate baseline machine learning models.
- Write maintainable Python code using pandas, NumPy, and scikit-learn.
- Apply ML evaluation metrics and identify issues such as overfitting.
- Assist with integrating models into applications, APIs, and data pipelines.
- Participate in code reviews, testing, debugging, and documentation.
- Collaborate with senior engineers and cross-functional teams.
- Communicate progress, raise blockers early, and apply feedback.Must-Haves
- Ideally 6–12 months of relevant professional, internship, co-op, or equivalent hands-on experience.
- Maximum 2 years of total experience — candidates exceeding this are not eligible.
- Foundational supervised and unsupervised machine learning knowledge.
- Python, pandas, NumPy, and scikit-learn.
- Basic SQL and structured data handling.
- Fundamentals of Git, testing, debugging, and modular coding.
- Strong communication, adaptability, and willingness to learn.Nice-to-Haves
- Academic or internship projects covering data preparation through model evaluation.
- Exposure to deep learning or generative AI.
- REST APIs, cloud platforms, notebooks, containers, or CI/CD.
- Git pull requests and code reviews.
- Responsible AI concepts, including bias, privacy, and explainability.