Engineering Manager-AI/ML at SPRINGML, INC. DBA EGEN SOLUTIONS

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

01 Feb, 26

Salary

0.0

Posted On

03 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, AI, Data Science, Software Engineering, Natural Language Processing, Computer Vision, Python, Numpy, Pandas, TensorFlow, PyTorch, Scikit-learn, Google Cloud, Generative AI, Document Extraction, Conversational AI

Industry

IT Services and IT Consulting

Description

Job Description : We are seeking a highly motivated and experienced ML Manager/Lead Data Scientist to join our growing ML/GenAI team. You will play a key role in designing, developing and productionalizing ML/GenAI applications by evaluating models, training and/or fine tuning them. As a lead, you require a deep understanding of both machine learning algorithms and software architecture. You need to take ownership of projects from conception to deployment, collaborating with engineers and stakeholders to ensure successful project delivery. \n 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, model evaluation, and inference serving. Proven experience building and deploying machine learning models in production environments for real life applications Good understanding of natural language processing, computer vision or other deep learning techniques. Expertise in Python, Numpy, Pandas and various ML libraries (e.g., XGboost, TensorFlow, PyTorch, Scikit-learn, LangChain). Familiarity with Google Cloud or any other Cloud Platform and its machine learning services. Excellent communication, collaboration, and problem-solving skills. Good to Have Google Cloud Certified Professional Machine Learning or TensorFlow Certified Developer certifications or equivalent. Experience of working with one or more public cloud platforms - namely GCP, AWS or Azure. Experience with AutoML and vision techniques. Master’s degree in statistics, machine learning or related fields. \n

Responsibilities

You will design, develop, and productionalize ML/GenAI applications, taking ownership of projects from conception to deployment. Collaborate with engineers and stakeholders to ensure successful project delivery.