ML Engineer AI Solutions
ML Engineer AI Solutions
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Job Description
Job Description: ML Engineer AI Solutions
We are seeking a highly skilled Machine Learning Engineer with 37 years of experience from a Tier1 institute to join our team at Wadhwani Foundation. You will develop enterprisegrade scalable ML models and robust pipelines while working under senior ML scientists and with software engineers. Your role involves building rigorously designed solutions that address societal challenges and serve as reliable decisionmaking tools for our partners. Youll be responsible for ensuring data integrity integrating backend solutions and optimizing AI workflows to create impactful trustworthy systems that can be effectively deployed across the Foundations diverse domains of interest.
Roles and Responsibilities:
- Have a strong research background and are adept at a variety of data mining/analysis methods and tools building and implementing models visualizing data creating/using algorithms and running simulations.
- Have enough education and experience to be able to quickly recognize a bad idea and lay out a process for designing and testing a potentially good one.
Should be able drive endtoend AI development from early PoC experimentation to production deployment. Responsibilities include building reproducible ML environments developing ETL pipelines setting up feature stores implementing CI/CD for models and ensuring continuous model monitoring and optimization. Ideal candidates should have experience with ML infrastructure model serving A/B testing API integrations and performance tuning for scalable AI solutions.
- Develop robust Machine Learning (ML) solutions leveraging LLMs RetrievalAugmented Generation (RAG) and AIdriven analytics.
- Design and implement backend integrations for ML models ensuring seamless deployment API development microservices architecture and cloudbased scalability.
- Curate and preprocess structured and unstructured agricultural datasets transforming them into MLready formats for training and validation.
- Contribute to algorithm development and scaled deployment while defining metrics to evaluate model performance efficiency and realworld impact.
- Develop Finetuned / custom LLMs using frameworks like Hugging Face LangChain and LlamaIndex optimizing RAG pipelines for domainspecific knowledge retrieval.
- Build and manage MLOps & LLMOps pipelines ensuring automated model training deployment monitoring and lifecycle management for scalable and reliable AI systems.
- Are comfortable working with crossfunctional teams and have excellent communication skills and a track record of driving projects to completion.
- Have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good.
- Stay updated with cuttingedge ML AI MLOps LLMOps and backend trends integrating best practices to enhance reliability efficiency and scalability.
Desired Qualification : B.E. / B.Tech Computer Science / Electronics / Electrical Engineering from a Tier1 Engineering institute.
Skillset :
- Machine Learning and Deep Learning: Supervised Semisupervised unsupervised and reinforcement learning techniques Feature engineering model training hyperparameter tuning CNNs LSTM GRU RNNs Transformers and Attention Mechanisms NLP Computer Vision and TimeSeries Forecasting
- Programming:
Handson experience with Python libraries
- Popular neural network libraries
- Popular data science libraries (Pandas numpy)
o Knowledge of systemslevel programming. Under the hood knowledge of C or C
o Experience and knowledge of various tools that fit into the model building pipeline. There are several you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
o Database concepts; SQL NoSQL
- AI Frameworks: LangChain LlamaIndex Hugging Face TensorFlow/PyTorch
- Backend & Deployment: FastAPI Flask Docker Kubernetes CI/CD Pipelines
- Data Engineering: ETL/ELT Data Lake Lakehouse architecture Vector Databases
- Cloud Platforms: AWS/GCP/Azure API Gateway Serverless Computing
- Data Annotation Tools: Labelbox Scale AI Prodigy V7 Amazon SageMaker Ground Truth etc.
- LLMs & RAG Prompt Engineering LLM Finetuning setting up RAG pipelines. Experience working with leading proprietary and open source LLMs for developing enterprise grade applications.
- Experience Designing and Executing Agentic AI Workflows for an enterprise or social sector use case would be an added plus.
n Deep experience in pedagogy and emerging techniques in adult skilling n Experience in the use of technology in large scale skilling efforts n Creation of highly engaging, world-class content for global audiences n Training of relevant teams on content and content delivery n Use of analytical methods to support content development and refinement n Conducting market assessments to identify best practices and gaps n Maintaining a strong network of content professionals to get real-time perspectives on opportunities and solutions Engagement with various stakeholders to learn about unique needs and considerations of sub-segments
Education
Bachelors in Technology / Masters in Technology
Employment Type
Full Time
