Machine Learning Engineer
- Posted 27 days ago
- Be among the first 40 applicants
Job Description
Machine Learning Engineer
Location: Bangalore
Experience:2–3 Years
Employment Type: Full-time
About the Role
We are looking for a Machine Learning Engineer to design, develop, and deploy advanced machine learning systems focused on forecasting, optimization, and AI-driven solutions.
The ideal candidate will have a strong foundation in Mathematics, Statistical Modeling, Machine Learning, and Large Language Models (LLMs), with the ability to translate complex problems into scalable, production-ready solutions.
Key Responsibilities
- Design and implement end-to-end ML pipelines for production environments.
- Develop forecasting and optimization models using advanced mathematical and statistical techniques.
- Build, pre-train, fine-tune, and evaluate ML and LLM-based models.
- Apply strong knowledge of probability, statistics, linear algebra, calculus, and optimization to solve complex problems.
- Develop and integrate LLM and Generative AI solutions into production workflows.
- Conduct structured experimentation, model validation, and performance optimization.
- Work with large-scale and real-time datasets to build predictive systems.
- Collaborate with cross-functional teams to integrate ML models into live workflows.
- Build scalable and low-latency ML infrastructure.
- Maintain technical documentation for reproducibility and maintainability.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Statistics, Data Science, or a related field.
- 2–3 years of hands-on experience in developing and deploying ML models in production.
- Strong proficiency in Python.
- Experience with PyTorch, TensorFlow, and scikit-learn.
- Strong foundation in Mathematics, including probability, statistics, linear algebra, calculus, optimization, and mathematical modeling.
- Good understanding of time-series forecasting, statistical learning, predictive modeling, and model evaluation.
- Hands-on exposure to LLMs, Generative AI, NLP, fine-tuning, prompt engineering, or LLM evaluation.
- Experience with Git, Docker, and Kubernetes.
- Strong analytical and problem-solving skills with a focus on experimentation and validation.
Good to Have
- Exposure to Reinforcement Learning.
- Experience in portfolio optimization, quantitative modeling, or signal generation.
- Experience working with real-time or large-scale datasets.
- Knowledge of LLM inference optimization and production deployment.
More Info
Key Skills
scikit-learn
Model Evaluation
LLM Evaluation
Generative AI
Signal Generation
Time-series Forecasting
LLM Inference Optimization
Fine-tuning
Prompt Engineering
Large Language Models (LLMs)
About Company
Skills:
Tensorflow, Pytorch, Python, MLops, Spark, Databricks, GPU infrastructure, model versioning, event tokenization, efficient attention, attention-based architectures, positional embeddings, mixed-precision training, self-supervised pretraining, gradient accumulation, automated retraining, experiment tracking, masked modeling, deep learning models, distributed strategies, contrastive learning, check-pointing, downstream fine-tuning, Production Monitoring
Skills:
Tensorflow, Nlp, Jax, Pytorch, Python, ASR, TTS, Llm
Skills:
Machine Learning, Deep Learning, Tensorflow, Pytorch, Opencv, Python, Computer Vision, GenAI, Prompting, LLMs, GenAI systems, OCR engines, PIL, Fine-tuning
Skills:
Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow
Skills:
classification), NLP (tokenization, Oops, C, C++, Os Fundamentals, Objective-c, Tensorflow, Pytorch, Swift, Python, language modeling, modern agentic and multi-agent systems, decoding, RAG architectures