AI Developer

Qtsolv; Pune District; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

Qtsolv in Pune seeks a Senior AI Developer to design, develop, and deploy AI solutions using Edge LLMs, Prompt Engineering, RAG, and LLM integrations. You will build production-ready GenAI apps and ensure reliability with safety-focused practices.

You will design edge deployments, optimize latency, and collaborate with cross-functional teams to translate business needs into scalable AI services using Python and modern AI frameworks.

Qualifications

• 7+ years of software development experience with at least 3-4 years in AI/ML or Generative AI development.

Responsibilities

• Develop and deploy Edge LLM-based applications optimized for performance, latency, and resource-constrained environments.
• Design and implement advanced Prompt Engineering strategies to improve LLM response quality, accuracy, and consistency.
• Build and maintain RAG pipelines using vector databases, embeddings, and enterprise knowledge sources.
• Integrate LLMs with existing applications, products, APIs, and enterprise systems.
• Develop scalable AI services and microservices using Python and modern AI frameworks.
• Collaborate with cross-functional teams to gather requirements and translate business needs into AI-powered solutions.
• Apply Functional Safety Engineering principles during the development lifecycle to ensure system reliability and compliance.
• Participate in DFMEA activities to identify potential failure modes, assess risks, and implement mitigation strategies.
• Conduct model evaluation, testing, monitoring, and performance optimization for deployed AI solutions.
• Document AI architectures, workflows, and technical specifications.

Skills

Edge LLMs

Prompt Engineering

RAG

LLM integrations

Python

PyTorch/TensorFlow

REST APIs

Functional Safety Engineering

DFMEA

Cloud platforms

Containerization

Tools

LangChain

LlamaIndex

Vector databases

PyTorch

TensorFlow

Job description

We are looking for a highly skilled Senior AI Developer to design, develop, and deploy AI solutions leveraging Edge LLMs, Prompt Engineering, Retrieval-Augmented Generation (RAG), and LLM integrations. The ideal candidate will have experience building production-ready GenAI applications and an understanding of Functional Safety Engineering and DFMEA practices for developing reliable and robust AI-enabled systems.

Key Responsibilities

• Develop and deploy Edge LLM-based applications optimized for performance, latency, and resource-constrained environments.
• Design and implement advanced Prompt Engineering strategies to improve LLM response quality, accuracy, and consistency.
• Build and maintain RAG pipelines using vector databases, embeddings, and enterprise knowledge sources.
• Integrate LLMs with existing applications, products, APIs, and enterprise systems.
• Develop scalable AI services and microservices using Python and modern AI frameworks.
• Collaborate with cross-functional teams to gather requirements and translate business needs into AI-powered solutions.
• Apply Functional Safety Engineering principles during the development lifecycle to ensure system reliability and compliance.
• Participate in DFMEA activities to identify potential failure modes, assess risks, and implement mitigation strategies.
• Conduct model evaluation, testing, monitoring, and performance optimization for deployed AI solutions.
• Document AI architectures, workflows, and technical specifications.

Required Skills

7+ years of software development experience with at least 3-4 years in AI/ML or Generative AI development.

• Strong experience with Edge LLMs, LLM deployment, and inference optimization.
• Hands‑on expertise in Prompt Engineering, RAG, LangChain, LlamaIndex, and vector databases.
• Experience integrating LLMs such as Llama, Gemma, Mistral, GPT, or similar foundation models.
• Proficiency in Python and AI/ML frameworks such as PyTorch or TensorFlow.
• Experience building and consuming REST APIs and integrating AI solutions into enterprise applications.
• Understanding of Functional Safety Engineering concepts and risk assessment methodologies.
• Knowledge of DFMEA processes and failure analysis techniques.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization technologies.

Preferred Qualifications

• Experience in Automotive, Industrial Automation, Robotics, Embedded Systems, or other safety‑critical domains.
• Knowledge of MLOps, CI/CD pipelines, model monitoring, and AI governance.
• Experience with edge computing platforms and embedded AI deployments.
• Understanding of AI explainability, validation, and testing methodologies.