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Generative AI Engineer – LLM & Enterprise Knowledge Systems
Generative AI Engineer – LLM & Enterprise Knowledge Systems
aurusai's logo

Generative AI Engineer – LLM & Enterprise Knowledge Systems

Uday Ayyagari's profile picture
Posted by Uday Ayyagari
4 - 10 yrs
₹10000L - ₹1500000L / yr
Remote only
Skills
Generative AI
Retrieval Augmented Generation (RAG)
Prompt engineering
Natural Language Processing (NLP)
OpenAI
Large Language Models (LLM) tuning
Llama
Cloudera
PEFT (Parameter-Efficient Fine-Tuning)
Model-View-View-Model (MVVM)

About the Role:

We are looking for an experienced and imaginative Generative AI Architect & Engineer to lead, design, and build cutting-edge solutions using Large Language Models (LLMs) such as OpenAI’s GPT-4/5, Google Gemini, Meta’s LLaMA, and Anthropic Claude. You will spearhead the development of a corporate-wide GenAI platform that harnesses proprietary enterprise knowledge and empowers sales, operations, and other departments with instant, contextual insights, guidance, and automation.

As the GenAI leader, you will be responsible for building scalable pipelines, integrating enterprise content repositories, fine-tuning or adapting foundational models, and creating secure and intuitive access patterns for business teams. You are passionate about creating highly usable solutions that solve real-world problems, and are fluent across architecture, implementation, and MLOps best practices.


Key Responsibilities:

  • Architect and implement an end-to-end GenAI solution leveraging LLMs to serve as a contextual assistant across multiple business units.
  • Develop pipelines to ingest, clean, and index enterprise knowledge (documents, wikis, CRM, chat transcripts, etc.) using RAG (Retrieval-Augmented Generation) patterns and vector databases.
  • Lead fine-tuning, prompt engineering, and evaluation of LLMs, adapting open-source or commercial models to enterprise needs.
  • Design a secure, scalable, API-first microservice platform, including middleware and access control, integrated into corporate systems.
  • Work closely with sales, operations, and customer support teams to gather use cases and translate them into impactful GenAI features.
  • Drive experimentation and benchmarking to evaluate various open and closed LLMs (OpenAI, Claude, Gemini, LLaMA, Mistral, etc.) for best performance and cost-efficiency.
  • Collaborate with DevOps teams to enable MLOps workflows, CI/CD pipelines, versioning, and A/B testing for AI models.
  • Contribute to technical documentation, best practices, and internal knowledge sharing.


Key Qualifications:

  • 4–5+ years of hands-on experience in AI/ML product development or applied research.
  • Demonstrated experience working with LLMs (OpenAI, LLaMA, Claude, Gemini, Mistral, etc.) and RAG pipelines, including vector search (FAISS, Weaviate, Pinecone, Chroma, etc.).
  • Strong Python skills and experience with frameworks such as LangChain, LlamaIndex, Transformers, Ray, HuggingFace, or equivalent.
  • Deep understanding of NLP, model fine-tuning, embeddings, tokenization, and content ingestion pipelines.
  • Exposure to enterprise content systems (e.g., SharePoint, Confluence, Salesforce, internal wikis, etc.) and integrating with them securely.
  • Solid foundation in software architecture, microservices, API design, and cloud deployments (Azure, AWS, or GCP).
  • Experience with security, RBAC, and compliance practices in enterprise-grade solutions.
  • Ability to lead projects independently and mentor junior engineers or data scientists.


How to Apply:

Submit your resume and a short technical project summary or portfolio (GitHub, Hugging Face, blog posts)

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About aurusai

Founded :
2018
Type :
Product
Size :
0-20
Stage :
Raised funding

About

aurus.ai is a category-defining AI-as-a-Service (AIaaS) platform with a mission to commoditize enterprise-grade AI, making powerful LLM-based capabilities accessible, relevant, and affordable for businesses of all sizes. From automating complex financial, supply chain or insurance document workflows to enabling real-time decisioning from unstructured data, our platform turns friction into functionality. We deploy a modular, functional architecture that empowers SMBs and enterprises alike to plug into the future of intelligent automation—quickly, securely, and at scale.

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Candid answers by the company

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Aurus.ai is an enterprise AI platform that helps businesses automate document-heavy workflows, run predictive risk assessments, and interact with data through natural language. Its products include DoKrunch (document intelligence and automation), RiskLens (risk scoring and predictive modeling), and Cortus (a conversational interface to “speak with your data”). Aurus.ai serves SMBs and enterprises across sectors like finance, insurance, and supply chain, offering faster processing, higher accuracy, and better ROI through AI-driven automation and decision support.

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Job Title: Senior AI/ML Engineer/Team Lead

Location: Gurugram, Haryana

Employment Type: Full-Time

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About Aaizel Tech

Aaizel Tech is a pioneering tech startup at the intersection of cybersecurity, AI, geospatial solutions, and more. We drive innovation by delivering transformative technology solutions across industries. As a growing startup, we are looking for passionate and versatile professionals eager to work on cutting-edge projects in a dynamic environment.



Role Overview

As a Senior AI/ML Engineer at Aaizel Tech, you will lead the design, development, and deployment of advanced Machine Learning models and AI solutions. You will work on projects ranging from predictive analytics and NLP to computer vision and anomaly detection. You will also mentor a team of AI/ML professionals, collaborate with cross-functional teams, and drive innovation by integrating state-of-the-art research with scalable production systems.



Key Responsibilities


1. Model Development & Optimization

Design & Implementation:

  • Architect and develop end-to-end ML solutions for applications such as predictive analytics, anomaly detection, computer vision, and NLP.
  • Utilize advanced techniques including deep learning (CNNs, RNNs), reinforcement learning, and generative models (GANs) to address complex challenges.

Optimization:

  • Fine-tune model parameters using techniques such as hyperparameter tuning (Grid Search, Bayesian Optimization, Neural Architecture Search).
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2. Advanced Data Engineering & Integration

Data Pipeline Development:

  • Build robust ETL pipelines using libraries like Pandas, NumPy, and PySpark to process large-scale datasets from satellite imagery, IoT sensors, and real-time streams.
  • Integrate data from diverse sources (APIs, databases, big data platforms like Hadoop and Apache Kafka) to support real-time analytics.

Data Quality & Preprocessing:

  • Implement data cleansing, feature engineering, and transformation pipelines to ensure high-quality inputs for ML models.


3. Research & Innovation

Algorithm Research:

  • Conduct research on state-of-the-art ML techniques including Transfer Learning, Transformer models, and AutoML to enhance model performance.
  • Innovate new algorithms for specialized tasks such as geospatial analysis, environmental modeling, or cybersecurity threat detection.

Prototyping & Experimentation:

  • Develop proof-of-concept models and prototypes to validate new approaches before production deployment.


4. Deployment, MLOps & Performance Monitoring

Model Deployment:

  • Deploy models using containerization (Docker) and orchestration tools (Kubernetes) to ensure scalable and efficient production environments.
  • Work with cloud platforms (AWS, Azure, GCP) and model serving solutions (TensorFlow Serving, ONNX, TorchServe) for high-throughput inference.

MLOps & Lifecycle Management:

  • Implement CI/CD pipelines for ML models, ensuring seamless updates and versioning.
  • Develop monitoring dashboards (using Prometheus, Grafana) to track model performance and trigger retraining based on real-time feedback.


5. Collaboration & Leadership

Cross-Functional Teamwork:

  • Collaborate closely with data engineers, software developers, domain experts, and product managers to integrate AI solutions into end-to-end products.

Mentorship & Code Quality:

  • Provide technical leadership and mentorship to junior AI/ML engineers, ensuring adherence to coding standards and best practices.
  • Participate in code reviews, maintain detailed documentation, and foster a culture of continuous learning.


Recommended Technology Stack

Backend Framework:

  • Python (Django/FastAPI): Ideal for API integration, leveraging Python’s rich AI/ML ecosystem.

AI/ML Frameworks:

  • PyTorch + Hugging Face Transformers + scikit-learn: For flexibility in research, multilingual NLP tasks, and classical ML pipelines.

Data Engineering:

  • Apache Kafka + Apache Spark + Apache NiFi: To handle both real-time data streaming and batch processing.

Database & Storage:

  • PostgreSQL with TimescaleDB extension: For structured and time-series data storage.

DevOps & Monitoring:

  • Docker, Kubernetes, GitLab CI/CD, Prometheus/Grafana: For containerized deployments, continuous integration, and comprehensive monitoring.

Media Processing:

  • OpenCV, FFmpeg, Tesseract OCR, Wav2Vec2: To support image, video, and speech-to-text processing where needed.


Required Skills & Qualifications

Technical Expertise:

  • Experience:
  • 4+ years in Machine Learning, AI research, or a related field with a proven track record of delivering production-level AI solutions.
  • Programming & Frameworks:
  • Expertise in Python and hands-on experience with frameworks like PyTorch, TensorFlow, and scikit-learn.
  • Experience with Hugging Face Transformers for NLP applications.
  • Data Engineering:
  • Proficiency in building data pipelines using Pandas, NumPy, PySpark, and integrating data from diverse sources.
  • Familiarity with big data platforms and real-time data processing frameworks.
  • Model Deployment & MLOps:
  • Hands-on experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for ML models.
  • Experience with cloud deployment and model serving solutions.
  • Research & Innovation:
  • Demonstrated ability to apply advanced ML techniques (deep learning, transfer learning, reinforcement learning) to solve real-world problems.
  • Testing & Optimization:
  • Strong background in model evaluation, hyperparameter tuning, and performance optimization.

Soft Skills:

  • Exceptional problem-solving and analytical abilities.
  • Strong communication skills, with the ability to present complex technical concepts to diverse stakeholders.
  • Leadership and mentoring experience, with a collaborative approach to working in cross-functional teams.
  • Ability to thrive in a fast-paced, dynamic environment and drive continuous innovation.

Educational Background:

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field from a reputed institution.


What We Offer

  • Innovative Projects: Engage in cutting-edge AI/ML projects that influence product strategy and technological innovation.
  • Professional Growth: Opportunities for continuous learning, mentorship, and career advancement.
  • Collaborative Culture: Work within a diverse team of experts passionate about pushing the boundaries of technology.
  • Impactful Work: Play a key role in shaping AI-driven solutions and driving real-world impact.


Join Aaizel Tech as a Senior AI/ML Engineer and lead the development of innovative, scalable AI solutions that transform industries and drive digital excellence!

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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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