As an AI/ML Engineer, you will research and select the right ML/LLM models and turn them into working integrations for our products and our customers systems (SaaS, portals, ecommerce, CRM/ERP, mobile apps, internal tools). You will own endtoend delivery: from understanding the business problem to building workflows (e.g., n8n/Make) and deploying and maintaining AI features in production.
This is a handson engineering role: you should enjoy building and deploying real AI features and integrations more than only doing analytical research or dashboards.
Position: AI/ML Engineer
Number of Positions: Two
Experience: Open to all levels (from strong fresher to experienced)
Location: Surat, Gujarat (INDIA)
Roles & Responsibilities
- Research and evaluate ML/LLM and Vision models that can improve our internal operations and our customers business processes (SaaS, portals, ecommerce, CRM/ERP, mobile apps).
- Evaluate, develop, optimize, use and selfhost models needed for various jobs.
- Design and build AI integrations for CRM, ERP, inventory, and other business systems, including Model Context Protocol (MCP) based integrations where relevant.
- Implement AIdriven workflows using tools like n8n, Make, Zapier, etc., and connect them with APIs, databases, and thirdparty services.
- Build Vision ML pipelines for video feeds and images (face/object detection, matching, analytics, quality checks, etc.).
- Develop search and ecommerce features using LLMs and retrieval (Apache Solr, Elasticsearch, vector search) and connect them to frontend apps and backoffice tools.
- Create WhatsApp and multichannel automations powered by LLMs and other AI models.
- Monitor, debug, and improve AI workflows and integrations running in production environments.
Required Skills
- Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field.
- Handson experience in building and deploying machine learning or LLMbased features in real applications (internships, projects, or production experience).
- Strong programming skills in Python and experience with deep learning and ML frameworks such as TensorFlow and/or PyTorch, plus common data science libraries (NumPy, pandas, scikitlearn, etc.).
- Strong experience working with REST APIs, webhooks, and integrating thirdparty systems (CRM/ERP/Inventory/WhatsApp/etc.).
- Handson experience building automations with tools like n8n, Make, or similar low/nocode workflow platforms.
- Comfort deploying models and services (Docker, cloud, simple MLOps) and wiring them into existing products.
- Solid understanding of natural language processing (NLP) and deep learning techniques, including working with LLMs and transformer-based architectures.
- Practical experience with LLM APIs (e.g., OpenAI, Gemini, etc.) for building real applications and workflows.
- Knowledge of data engineering concepts: data preprocessing, ETL/ELT pipelines, data quality, and working with relational and NoSQL databases.
- Familiarity with retrieval and search technologies (e.g., Apache Solr, Elasticsearch, vector search / vector databases) is a strong plus.
- Exposure to Vision ML (image/video processing, classical CV and/or deep learningbased CV) is a plus; strong interest here is also valued.
- Familiarity with cloud platforms (AWS, Azure, GCP) and deploying ML models or data pipelines in cloud environments.
- Strong research mindset, problemsolving skills, and ability to design experiments, interpret results, and iterate.
- Excellent communication and collaboration skills, comfortable working in crossfunctional product and client teams.
Nice-to-have / Bonus
- Experience building AI features specifically for ecommerce (search, recommendation, personalization, chatbot, fraud/risk scoring, review analysis) or SaaS products (copilots, analytics insights, smart workflows)
- Experience with MLOps tools and practices (model versioning, CI/CD for ML, monitoring, observability).
- Prior work in a consulting, services, or staff augmentation context, managing multiple projects or stakeholders.
- Please include 23 concrete examples (links or brief descriptions) of AI/LLM or automation projects you have built or contributed to (e.g., GitHub repos, demos, case studies, Kaggle, hackathons).
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