AI Engineer

Vassar Labs; Hyderabad; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Vassar Labs in Hyderabad is seeking an AI Engineer with 3+ years of experience to design and deploy production-grade AI/ML solutions for climate-tech applications. You will work across ML, DL, NLP, and Generative AI, focusing on correctness, scalability, and measurable impact.

You will write Python, build data pipelines with SQL, deploy models via REST APIs, and collaborate with Data Scientists, Software Engineers, and domain experts to translate requirements into robust AI systems.

Qualifications

• 3+ years of experience in AI/ML with production-grade solutions.
• Strong foundations in Python and software engineering.
• Proficiency with SQL and data systems for scalable pipelines.
• Experience with ML/DL frameworks (PyTorch or TensorFlow).
• Familiarity with Generative AI, NLP, and CV applications.
• Experience deploying models via APIs and MLOps tools.

Responsibilities

• Design, develop, train, validate, and deploy AI/ML models for use cases.
• Write clean, efficient Python code for data and inference workloads.
• Work with structured and unstructured data including time-series.
• Build data preparation workflows with Python and SQL.
• Apply ML methodology for model selection, tuning, and evaluation.
• Develop solutions using ML, DL, NLP, CV, and Generative AI.
• Build and optimize AI pipelines, APIs, and batch/stream components.
• Profile and optimize code, queries, model inference, latency.
• Follow modular design, testing, version control, and reproducibility.
• Work with cloud, databases, containers, and MLOps tools.
• Monitor models for quality, drift, reliability, and resource usage.
• Collaborate with cross-functional teams and document technical details.

Skills

Python

Software Engineering

SQL

Data Systems

Statistics

Machine Learning

Deep Learning

NLP

Computer Vision

Generative AI

ML Ops

REST APIs

Docker

Cloud Platforms

Tools

NumPy

Pandas

Scikit-learn

PyTorch

TensorFlow

Git

Kubernetes

Job description

We are looking for an AI Engineer with 3+ years of experience and with strong foundations in Python/software engineering, SQL and data systems, statistics, and machine learning to design, develop, and deploy production-grade AI/ML solutions for real-world business and climate-tech applications. The role spans classical Machine Learning, Deep Learning, Generative AI, and production engineering, with an emphasis on correctness, scalability, maintainability, and measurable impact.

Key Responsibilities:

• Design, develop, train, validate, and deploy AI/ML models for business and domain-specific use cases.
• Write clean, efficient, testable Python and select appropriate synchronous, concurrent, asynchronous, or parallel execution patterns for data and inference workloads.
• Work with structured and unstructured data including tabular data, text, images, geospatial data, and time-series data.
• Build reliable data preparation workflows using Python and SQL, including data cleaning, transformation, feature engineering, and validation.
• Apply sound ML methodology for model selection, training, hyperparameter tuning, cross-validation, error analysis, and evaluation.
• Develop solutions using Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI/LLMs where appropriate.
• Build and optimize AI pipelines, model inference workflows, REST APIs, and batch/stream processing components for production deployment.
• Profile and optimize code, database queries, model inference, memory usage, throughput, and latency based on measurable bottlenecks.
• Use software engineering practices such as modular design, unit/integration testing, version control, code review, logging, and reproducible experiments.
• Work with cloud platforms, databases, containers, and MLOps tools to deploy and operate scalable AI solutions.
• Monitor deployed models and services for model quality, data drift, reliability, scalability, latency, and resource utilization.
• Collaborate with Data Scientists, Software Engineers, Product/Domain Experts, and Project Teams to translate requirements into robust AI solutions and maintain clear technical documentation.

Core Foundations & Required Skills:

Python & Software Engineering

• Strong command of Python fundamentals including data structures, functions, OOP, modules/packages, exception handling, typing, iterators/generators, decorators, context managers, and the standard library.
• Practical understanding of concurrency and parallelism: threading, multiprocessing, asyncio, concurrent.futures, synchronization primitives, queues, race conditions, deadlocks, and safe shared-state handling.
• Understanding of the Python GIL and the ability to choose appropriate approaches for I/O-bound versus CPU-bound workloads.
• Good knowledge of data structures, algorithms, time/space complexity, debugging, profiling, unit testing, and writing maintainable production code.
• Comfort with Linux command-line workflows, Git-based development, REST APIs, and common software engineering practices.

SQL & Data Foundations

• Strong SQL skills including joins, subqueries, CTEs, aggregations, GROUP BY/HAVING, window functions, conditional logic, and working with large datasets.
• Understanding of relational database fundamentals including schema design, normalization, primary/foreign keys, transactions/ACID, indexes, and query execution plans.
• Ability to diagnose and optimize slow queries and avoid common data-access problems such as unnecessary scans, repeated queries, and inefficient joins.
• Hands-on data manipulation using libraries such as NumPy and Pandas, with awareness of vectorization, memory usage, missing data, outliers, and data quality checks.

Machine Learning & Statistics Foundations

• Strong understanding of supervised and unsupervised learning, including regression, classification, clustering, dimensionality reduction, and common tree/ensemble methods.
• Working knowledge of probability and statistics concepts used in ML, including distributions, sampling, descriptive statistics, correlation, hypothesis testing, and uncertainty.
• Understanding of loss/objective functions, gradient-based optimization, bias-variance trade-off, overfitting/underfitting, regularization, feature selection, and hyperparameter tuning.
• Strong model validation practices: train/validation/test splits, cross-validation, data leakage prevention, class imbalance handling, baselines, and reproducibility.
• Ability to select and interpret appropriate evaluation metrics such as precision, recall, F1, ROC-AUC/PR-AUC, log loss, MAE/RMSE, and domain-specific metrics rather than relying on accuracy alone.
• Hands-on experience with Scikit-learn and at least one Deep Learning framework such as PyTorch or TensorFlow, with understanding of neural networks, backpropagation, optimizers, and training workflows.
• Knowledge of one or more applied AI areas such as NLP, Computer Vision, time-series modelling, or geospatial ML; familiarity with modern architectures such as CNNs and Transformers is preferred.
• Practical knowledge of Generative AI/LLMs, prompting, embeddings, retrieval, evaluation, and the limitations/risks of LLM-based systems.
• Experience with model serving, APIs, Docker, cloud platforms, logging/monitoring, and basic MLOps practices for reliable production deployment.
• Strong analytical problem-solving skills and the ability to explain technical trade-offs, debug failures systematically, and validate assumptions with data.

Good to Have:

• Experience with LLM frameworks and tooling such as LangChain, LlamaIndex, Hugging Face, or equivalent.
• Experience with RAG, vector databases, embeddings, AI agents, tool/function calling, and systematic LLM evaluation.
• Exposure to distributed/data-processing systems such as Spark, Kafka, Ray, or equivalent.
• Exposure to Azure/AWS/GCP AI and ML services, Kubernetes, CI/CD, and production observability.
• Experience working with geospatial, satellite, climate, agriculture, water, or environmental datasets.
• Knowledge of model/inference optimization techniques, GPU serving, batching, quantization, caching, and production-scale AI systems.