AI Engineer

AI Engineer Responsibilities

As a selected associate, your daily tasks will include:

• Engaging in data science, AI, ML, LLM projects and analytics.
• Developing and implementing data models, AI, ML, deep learning, NLP, GenAI, LangChain, LLM, LLAMA, OpenAI, and GPT-based solutions.
• Managing data pipelines, ETL/ELT processes, and data warehousing.
• Utilizing Python and its libraries for advanced programming tasks.
• Handling data collection, management, cleaning, and transformation.
• Creating data visualizations using BI tools such as Power BI, Kibana, Grafana, and Looker Studio.
• Working with databases like MongoDB, Elasticsearch, Neo4j, Dgraph, Supabase, Bigquery, VectorDB, and SQL.
• Leveraging cloud platforms, including GCP, AWS, Azure, Linode, and Heroku.

Job Details

• Work Mode: Remote (Work From Home)
• Start Date: Immediate
• Duration: 6 months
• Stipend: ₹10,000 – ₹12,000 per month
• Industry: Information Technology & Services
• Employment Type: Probation of 6 Months followed by Full-time Position based on performance

Core Technical Skills

• Programming: Strong command of Python and TypeScript or JavaScript.
• Data and APIs: Experience building and consuming REST APIs, working with SQL, and handling messy enterprise databases (like PostgreSQL, Snowflake, or S3).
• Infrastructure: Working knowledge of cloud platforms (AWS, GCP) and continuous integration/continuous deployment (CI/CD) pipelines.

Applied AI Skills

• Large Language Models (LLMs): Hands-on experience calling model APIs, prompt engineering, and managing structured outputs.
• Advanced AI Systems: Familiarity with Retrieval-Augmented Generation (RAG), vector databases, agent orchestration, and evaluation suites.

Soft Skills and Business Acumen

• Problem-Solving in the Field: The ability to sit with a non-technical client, take a vague or messy business problem, and turn it into a working technical solution.
• Communication: Explaining complex technical trade-offs clearly to business stakeholders rather than just other engineers.
• High Ownership: Taking a project all the way from the first prototype to a stable production deployment inside a customer's environment.