Company

Technology

EngineeringManager-AI/ML

₹25–45L ~AI est. Hyderabad, India FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Manager candidates.

The Brief

“Engineering Manager-AI/ML. Skills: AI/ML, Generative AI, Machine Learning Engineering, Prompt Engineering. Design ML/AI applications. Build ML/AI applications”

Industry & Context.

Technology
Problems you'll solve

Problem-solving skills

What They're Looking For.

Must Have

5 years of experience in designing & building ML/AI applications, 8 years of Software engineering experience, 2 years of experience leading and mentoring ML/data science teams, Experience with Document extraction using AI, Experience with Conversational AI, Experience with Vision AI, Experience with NLP, Experience with Gen AI, Hands on customer experience with RAG solution, Hands on customer experience with fine tuning of LLM model, Proven experience building and deploying machine learning models in production

Nice to Have

Google Cloud Certified Professional Machine Learning, TensorFlow Certified Developer certifications, Experience working with GCP, Experience working with AWS, Experience working with Azure, Experience with AutoML, Experience with vision techniques, Master’s degree in statistics, Master’s degree in machine learning

What You'll Do.

Design ML/AI applications

Build ML/AI applications

Deploy ML/AI applications

Lead ML/data science teams

Mentor ML/data science teams

Operationalize ML models

Personalize ML models

Evaluate machine learning models

Tune machine learning models

Develop prompts for LLM

Analyze datasets for prompt development

Preprocess datasets for prompt development

Evaluate LLM responses

Improve LLM performance

Lead end-to-end design of Generative AI solutions

Lead architecture of Generative AI solutions

Design agentic workflows

Design model fine-tuning strategies

Build machine learning pipelines on GCP

Deploy machine learning pipelines on GCP

Perform exploratory data analysis

Analyze data distributions

Transform data into features

Write high-quality code

Build core components for prompt engineering

Build core components for vector search

Build core components for data processing

Build core components for model evaluation

Build core components for inference serving

Build machine learning models in production

Deploy machine learning models in production

How You'll Work.

Team & Collaboration

Collaborate with data scientists; Collaborate with engineers

Full Job Description

## What we're looking for ● At least 5 years of experience in designing & building ML/AI applications for customer and deploying them into production ● At least 8 years of Software engineering experience in building Secure, scalable and performant applications for customers. ● At least 2 years of experience leading and mentoring ML/data science teams( 4+ team members) ● Experience with Document extraction using AI, Conversational AI, Vision AI, NLP or Gen AI. ● Design, develop, and operationalize existing ML models by fine tuning, personalizing it. ● Evaluate machine learning models and perform necessary tuning. ● Develop prompts that instruct LLM to generate relevant and accurate responses. ● Collaborate with data scientists and engineers to analyze and preprocess datasets for prompt development, including data cleaning, transformation, and augmentation. ● Conduct thorough analysis to evaluate LLM responses, iteratively modify prompts to improve LLM performance. ● Lead the end-to-end design and architecture of scalable, reliable, and cost-effective Generative AI solutions. This includes designing RAG (Retrieval-Augmented Generation) pipelines, agentic workflows, and model fine-tuning strategies. ● Hands on customer experience with RAG solution or fine tuning of LLM model. ● Build and deploy scalable machine learning pipelines on GCP or any equivalent cloud platform involving data warehouses, machine learning platforms, dashboards or CRM tools. ● Experience working with the end-to-end steps involving but not limited to data cleaning, exploratory data analysis, dealing outliers, handling imbalances, analyzing data distributions (univariate, bivariate, multivariate), transforming numerical and categorical data into features, feature selection, model selection, model training and deployment. ● Act as the senior-most developer, writing clean, high-quality, and scalable code. This includes building core components for prompt engineering, vector search, data processing, m

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