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LLM Engineer/4-8 Years/ Hyderabad.
LLM Engineer/4-8 Years/ Hyderabad.

LLM Engineer/4-8 Years/ Hyderabad. at InRhythm Solutions · Hyderabad · 4 - 8 years · ₹10L - ₹20L / yr · Profitable · Posted 28 Jul 2025

InRhythm Solutions's logo

LLM Engineer/4-8 Years/ Hyderabad.

Naveen Kumar's profile picture
Posted by Naveen Kumar
4 - 8 yrs
₹10L - ₹20L / yr
Hyderabad
Skills
Large Language Models (LLM)

Key Responsibilities:

  • Develop and fine-tune large language models for diverse applications.
  • Implement and optimize LLMs to create efficient, scalable solutions.
  • Build intelligent agents that utilize LLMs to interact and perform complex analytical tasks.
  • Collaborate with cross-functional teams to integrate LLM technology into our products and services.
  • Conduct research to stay ahead of the latest trends and advancements in generative AI and machine learning.
  • Document and present model development processes and results for stakeholders.


Required Qualifications:

  • 4-8 years of experience in machine learning, specifically with generative AI and large language models.
  • Strong proficiency in Python and familiarity with AI and ML libraries such as TensorFlow, PyTorch, and Hugging Face Transformers.
  • Experience in fine-tuning LLMs and deploying AI models in a production environment.
  • Proven ability to design and implement AI agents using LLMs.
  • Excellent problem-solving skills and the ability to work in a fast-paced, dynamic environment.
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About InRhythm Solutions

Founded :
2005
Type :
Product
Size :
20-100
Stage :
Profitable

About

N/A

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● Data Preprocessing and Feature Engineering: Cleanse, preprocess, and transform raw data into suitable formats for training and testing AI/ML models. Perform feature engineering to extract relevant features from the data

● Model Training and Evaluation: Train and validate AI/ML models using diverse datasets to achieve optimal performance. Employ appropriate evaluation metrics to assess model accuracy, precision, recall, and other relevant metrics

● Data Visualization: Create clear and insightful data visualizations to aid in understanding data patterns, model behaviour, and performance metrics

● Deployment and Integration: Collaborate with software engineers and DevOps teams to deploy AI/ML models into production environments and integrate them into various applications and systems

● Data Security and Privacy: Ensure compliance with data privacy regulations and implement security measures to protect sensitive information used in AI/ML processes

● Continuous Learning: Stay updated with the latest advancements in AI/ML research, tools, and technologies, and apply them to improve existing models and develop novel solutions

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Location: Ahmedabad / Gandhinagar

Experience: 7+ Years

Employment Type: Full-Time

Kody Technolab Limited is seeking an experienced AI/ML Engineer to design, develop, and deploy

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• Design, develop, and deploy Machine Learning and Deep Learning models for classification,

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• Build and maintain end-to-end ML pipelines including data preprocessing, feature

engineering, model training, validation, evaluation, and deployment.

• Develop AI solutions using PyTorch, TensorFlow, Scikit-learn, Hugging Face, and related

frameworks.

• Work with Large Language Models (LLMs) and foundation models such as GPT, BERT, Llama,

Claude, and Stable Diffusion.

• Collaborate with product, engineering, and business teams to translate requirements into

scalable AI solutions.

• Optimize model performance, scalability, and reliability for production environments.

• Implement MLOps best practices using tools such as MLflow, Docker, Kubernetes, and Kubeflow.

• Stay updated with emerging trends and research in AI, ML, Deep Learning, and Generative AI.

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• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence,

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• 7+ years of hands-on experience in AI/ML product development.

• Strong proficiency in Python and ML frameworks including Scikit-learn, TensorFlow, PyTorch, and

Hugging Face.

• Experience with Generative AI, LLMs, GANs, VAEs, diffusion models, and prompt engineering.

• Strong understanding of the ML lifecycle including model training, tuning, deployment,

monitoring, and optimization.

• Experience with MLOps tools such as MLflow, Docker, Kubeflow, and CI/CD pipelines.

• Experience with AWS, Azure, or GCP cloud platforms.

• Strong problem-solving and analytical skills.Preferred Skills

• Fine-tuning and deployment of Large Language Models.

• Experience with RAG (Retrieval Augmented Generation) architectures.

• Contributions to open-source AI projects or research publications.

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Job Title: Senior AI Engineer

Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred

Employment Type: Full-time

Department: Artificial Intelligence / Engineering

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We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.

The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.

Key Responsibilities

1. AI Solution Design and Development

  • Design, develop, and deploy end-to-end AI and machine learning solutions.
  • Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
  • Develop scalable AI architectures and reusable components for enterprise applications.
  • Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.

2. Generative AI and LLM Engineering

  • Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
  • Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
  • Build AI agents and multi-step workflows using appropriate orchestration frameworks.
  • Implement prompt engineering, structured outputs, tool calling, and context management.
  • Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.

3. Machine Learning and Data Engineering

  • Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
  • Work with structured, unstructured, and multimodal data.
  • Build data preprocessing, feature engineering, model training, and validation pipelines.
  • Apply appropriate model evaluation techniques and statistical methods.

4. Production Deployment and MLOps / LLMOps

  • Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
  • Build automated CI/CD pipelines for AI applications and model deployment.
  • Implement model versioning, experiment tracking, monitoring, and performance evaluation.
  • Optimize inference latency, throughput, infrastructure utilization, and operating costs.
  • Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.

5. Software Engineering and System Architecture

  • Develop production-quality applications and APIs using Python and modern backend frameworks.
  • Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
  • Integrate AI services with enterprise applications, databases, and third-party systems.
  • Use relational databases, vector databases, caching, and distributed systems effectively.
  • Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.

6. Security, Quality, and Responsible AI

  • Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
  • Apply appropriate privacy, security, and compliance controls to AI systems.
  • Evaluate hallucinations, bias, reliability, and failure modes.
  • Design human-in-the-loop workflows and fallback mechanisms for critical use cases.

7. Technical Leadership and Collaboration

  • Lead technical design discussions and mentor junior AI engineers.
  • Define engineering standards, coding practices, and AI development guidelines.
  • Collaborate with cross-functional teams to deliver measurable business outcomes.
  • Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
  • Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.

Required Technical Skills

Programming and Backend Development

  • Strong proficiency in Python.
  • Experience with FastAPI, Flask, or Django.
  • Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
  • Experience with Git, automated testing, and CI/CD.

AI and Machine Learning

  • Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
  • Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
  • Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.

Generative AI and LLMs

  • Hands-on experience building production applications using LLM APIs or open-source models.
  • Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
  • Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
  • Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
  • Experience with tool calling, structured generation, agent workflows, and LLM evaluation.

Cloud and Infrastructure

  • Experience with AWS, Azure, or Google Cloud.
  • Working knowledge of Docker and Kubernetes.
  • Familiarity with model serving, scalable APIs, monitoring, and production deployments.
  • Experience with MLOps or LLMOps practices.

Databases and Data Processing

  • Strong SQL skills and experience with relational databases.
  • Familiarity with NoSQL databases, data pipelines, and distributed processing.
  • Experience working with unstructured data, document ingestion, and retrieval systems.

Preferred Qualifications

  • Experience with fine-tuning, quantization, or serving open-source LLMs.
  • Experience with multimodal AI, including text, image, audio, or video.
  • Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
  • Experience with AI security, guardrails, governance, and responsible AI.
  • Experience optimizing AI workloads for latency, scalability, and cost.
  • Contributions to open-source AI projects, research, or technical publications.

Education

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Key Success Metrics

  • Quality, accuracy, and reliability of deployed AI solutions.
  • Production adoption and measurable business impact.
  • Reduction in inference costs and response latency.
  • RAG retrieval quality and reduction in unsupported or incorrect responses.
  • System availability, scalability, and operational stability.
  • Delivery timelines, code quality, and technical mentoring effectiveness.
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Shubham Vishwakarma

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