Job Description

Location: Bangalore/Hyderabad/Remote

Department: Data Science & Analytics

Reports To: Cloud Solutions Architect/ML Solutions Architect

Position Overview:

We are seeking an experienced Machine Learning Engineer with a specialization in Time Series Modeling, Deep Learning, and Big Data technologies like PySpark. The ideal candidate should be adept at utilizing cloud platforms for deploying scalable solutions. This role will focus heavily on building, deploying, and monitoring predictive models, especially related to regression and time series forecasting.

Key Responsibilities:

Generative AI & LLMs
Prompt Engineering, Few-shot and Chain-of-Thought techniques
LLM Fine-Tuning (Full + LoRA/PEFT) with HuggingFace Transformers
RAG (Retrieval-Augmented Generation) architecture design and integration
Embedding generation and vector search using FAISS, Azure AI Search and similar tecniques
Hosted LLM services: Azure OpenAI, AWS Bedrock (Claude, Titan, Cohere)
Machine Learning & Deep Learning
o Transformers (BERT, RoBERTa, GPT-2/3/4, T5), CNNs, RNNs

o NLP/NLU tasks (NER, sentiment analysis, summarization, text generation)

o Model explainability (SHAP, LIME, feature importance)

o Time Series modeling: ARIMA, Prophet, DeepAR (SageMaker), Amazon Forecast, custom LSTM pipelines for forecasting and anomaly detection

Cloud-Native AI Engineering
Azure:
o Azure Machine Learning (workspaces, endpoints, designer, SDK), Azure AI Search (used in RAG), Azure Functions, AKS, ADF

AWS:
o Amazon SageMaker: built-in algorithms, custom model training, hyperparameter tuning jobs, processing pipelines, model registry, endpoint deployment (real-time + batch), model monitoring, Ground Truth for labeling

o AWS Glue (ETL), Lambda (triggered inference), EKS (Kubernetes orchestration)

MLOps & Observability
o End-to-end model lifecycle management using MLflow, Kubeflow, and SageMaker Pipelines

o CI/CD for model packaging, testing, and deployment (GitHub Actions, CodePipeline, Azure DevOps)

o Drift detection, model monitoring, usage metrics (token cost, latency, etc.)

Responsible AI practices and explainability pipelines
Data Engineering & Integration
o Streaming and batch pipelines using AWS Glue, Azure Data Factory, Kafka, and PySpark

o Storage systems: Amazon S3, Azure Data Lake Gen2

o Databases: Snowflake, MongoDB, Azure SQL, MySQL

Languages & Frameworks
o Python (primary), SQL, Bash

o HuggingFace Transformers, PyTorch, TensorFlow, LangChain, FastAPI

o Docker, Kubernetes (AKS/EKS), RESTful AP

Required Qualifications & Skills:

1. A bachelor's or higher degree in Computer Science, Statistics, Mathematics, or a related field.

2. Minimum of 3 years of hands-on experience in machine learning, with an emphasis on regression and time series modeling.

3. Proficiency with Python and its data science libraries (pandas, numpy, scikit-learn).

4. Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch.

5. Familiarity with PySpark or equivalent big data technologies.

6. Experience with cloud platforms like AWS, Azure, or GCP.

7. Experience with SQL Server.

8. Experience with Elasticsearch, Mongo is a bonus.

9. Strong problem-solving skills and a passion for innovation.

10. Excellent communication and interpersonal skills.

Preferred Qualifications:

Experience with end-to-end machine learning deployment in a cloud environment.

Publications or proven track record in the domain of time series analysis and/or using deep learning.

Previous work in a fast-paced tech/startup environment.

Job Type: Full-time

Pay: Up to ₹3,200,000.00 per year

Experience:

ML Engineer: 5 years (Required) PyTorch: 3 years (Required) pyspark: 3 years (Required) Python: 3 years (Required)
Work Location: In person