Artificial Intelligence Engineer (Hybrid-Gurgaon) US Timings

Position Overview:

AI Engineer / Developer

You will play a critical role in leveraging generative AI to extract structured insights from unstructured content, refine prompt engineering strategies, and build functional prototypes that bridge AI outputs with real-world applications. If you're passionate about NLP, LLMs, and building AI-first solutions, we want to hear from you.

Key Responsibilities:

• Document Intelligence

  : Leverage large language models (e.g., OpenAI GPT, Anthropic Claude) to analyze and extract meaningful information from various types of documents, including PDFs, contracts, compliance records, and reports.

• Data Structuring

  : Convert natural language outputs into structured data formats such as JSON, tables, custom templates, or semantic tags for downstream integration.

• Prompt Engineering

  : Design, write, and iterate on prompts to ensure high-quality, repeatable, and reliable responses from AI models.

• Tooling & Prototyping

  : Develop lightweight tools, scripts, and workflows (using Python or similar) to automate, visualize, and test AI interactions.

• Model Evaluation

  : Run controlled experiments to evaluate the performance of AI-generated outputs, identifying gaps, edge cases, and potential improvements.

• Pipeline Integration

  : Collaborate with software engineers and product teams to integrate LLM pipelines into broader applications and systems.

• Traceability & Transparency

  : Ensure each piece of extracted information can be traced back to its original source within the document for auditing and validation purposes.

Required Skills & Qualifications:

• Experience

  : Minimum of 3 years in AI/ML development, with a strong focus on

  natural language processing (NLP)

  ,

  document analysis

  , or

  conversational AI

  .

• LLM Expertise

  : Hands-on experience working with large language models (e.g., GPT-4, Claude, Mistral) and prompt-based interactions.

• Programming Skills

  : Proficient in

  Python

  and experienced with modern AI frameworks such as

  LangChain

  ,

  Hugging Face Transformers

  , or

  spaCy

  .

• Document Processing

  : Knowledge of

  embeddings

  ,

  chunking strategies

  , and

  vectorization techniques

  for efficient document indexing and retrieval.

• Vector Databases

  : Familiarity with

  FAISS

  ,

  Chroma

  ,

  Pinecone

  , or similar vector DBs for storing and querying embedding data.

• Analytical Mindset

  : Strong ability to design, run, and interpret structured tests to measure and enhance the accuracy of AI outputs.

Preferred Qualifications:

• RAG Workflows

  : Experience implementing

  Retrieval-Augmented Generation

  (RAG) systems for dynamic document querying and synthesis.

• Domain Exposure

  : Familiarity with

  legal

  ,

  regulatory

  , or

  compliance-based documents

  and the unique challenges they pose.

• LLMOps & Deployment

  : Exposure to deploying AI models or pipelines, including experience with

  web APIs

  ,

  LLMOps tooling

  , or

  cloud-native AI environments

  .