Senior AI Engineer - Agentic Systems & RAG | Devsinc

Devsinc; Lahore; Hybrid; PKR 2,800,000 - 6,000,000; Full time

Job summary

Devsinc Lahore is seeking a seasoned AI/ML engineer to own the end-to-end AI lifecycle, from experimentation to production. You will design autonomous AI agents, RAG systems, and scalable backend services using Python and modern ML stacks.

Responsibilities include building semantic search pipelines, optimizing inference, and guiding cloud deployments with security guardrails. You will mentor juniors and collaborate with enterprise clients to deliver measurable outcomes.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or related field.
• 4+ years of post-graduation AI/ML engineering experience shipping LLM apps to production.
• Strong Python proficiency with PyTorch and/or TensorFlow.
• Experience integrating commercial/open-source LLMs, including prompts and function calls.
• Hands-on with vector DBs and semantic search pipelines.

Responsibilities

• Design, develop, and deploy AI/ML apps including autonomous agents and tool-using systems.
• Build RAG and semantic search pipelines with embeddings and vector search.
• Develop production-grade backend APIs and real-time inference microservices.
• Improve model inference quality, latency, and cost via tuning and optimization.
• Lead MLOps and CI/CD pipelines with model versioning and testing.

Skills

Python & ML frameworks

LLM integration

Vector search

API & microservices

Cloud & DevOps

Security & evaluation

Education

Bachelor's degree in CS/SE/Data Science

Tools

pgvector

Pinecone

Weaviate

Qdrant

Milvus

Elasticsearch

FastAPI

PostgreSQL

MySQL

Docker

Git

CI/CD

AWS/Azure/GCP

LangChain/LlamaIndex

Hugging Face Transformers

vLLM/Ollama

LoRA/PEFT

MLOps tools (MLflow, Kubeflow)

Job description

In this role, you will own the end-to-end AI lifecycle—from initial experimentation, RAG system design, and prompt architecture to model evaluation, GPU inference optimization, containerized cloud deployment, and continuous MLOps monitoring. You will build high-throughput backend APIs, deploy autonomous AI agents with function-calling capabilities, and optimize retrieval pipelines over multi-modal vector stores. Working closely with enterprise clients, cross-functional engineering teams, and junior developers, you will guide technical decisions, mentor engineers, and ensure all AI systems are reliable, secure, cost-efficient, and aligned with measurable business outcomes.

Key Responsibilities

Generative AI Architecture, RAG & Agentic Workflows

• End-to-End AI System Design: Design, develop, and deploy AI/ML and LLM-based applications, including autonomous AI agents, tool-using systems, and human-in-the-loop decision workflows.
• RAG & Semantic Search Pipelines: Build, evaluate, and optimize Retrieval-Augmented Generation (RAG) and semantic search platforms using document chunking, embeddings, vector search, cross-encoder reranking, and grounding techniques.
• Backend API & Microservices Development: Construct robust, production-grade backend APIs and real-time inference microservices using Python (FastAPI, Flask, or Django) connected to PostgreSQL/SQL databases.
• Model Tuning & Optimization: Improve model inference quality, latency, throughput, and token costs through hyperparameter tuning, model quantization, dynamic batching, caching (Redis), and prompt optimization.

MLOps, Cloud Infrastructure & Governance

• Production MLOps & CI/CD: Implement automated testing, experiment tracking, model versioning, regression testing, and CI/CD deployment pipelines supported by benchmark evaluation datasets and human review loops.
• Cloud Architecture & Security: Guide cloud deployment across AWS, Azure, or GCP, enforcing security guardrails against prompt injection, data leakage, unauthorized access, and unsafe model outputs.
• Technical Mentorship & Client Leadership: Evaluate emerging AI technologies, mentor software engineers, document technical designs, and articulate complex architectural tradeoffs to clients and executive stakeholders.

Requirements

Technical Depth & Engineering Experience

• Educational Background: Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related quantitative discipline.
• Professional Experience: 4+ years of post-graduation professional experience in AI/ML engineering, with a proven track record of shipping LLM or generative AI applications into production environments.
• Python & ML Frameworks: Production-level Python proficiency paired with hands-on experience in PyTorch and/or TensorFlow, alongside deep knowledge of neural networks, NLP, and feature engineering.
• LLM Integration & Tooling: Practical experience integrating commercial and open-source LLMs—including prompt engineering, structured JSON outputs, function calling, and context window management.
• Vector Search Expertise: Hands-on experience building semantic search pipelines using vector databases such as pgvector, Pinecone, Weaviate, Qdrant, Milvus, or Elasticsearch.
• API & Database Engineering: Proven experience building RESTful microservices with FastAPI, Flask, or Django backed by SQL databases (PostgreSQL, MySQL).
• Cloud, DevOps & Containers: Proficiency with Git, Docker, CI/CD pipelines, and cloud platforms (AWS, Azure, or GCP), combined with MLOps best practices for model tracking and monitoring.
• Evaluation & Security Foundations: Strong understanding of LLM evaluation frameworks, regression testing, human-in-the-loop validation, and AI security risks.

Preferred Qualifications

• Experience with orchestration frameworks (LangChain, LlamaIndex, LangGraph) and LLM evaluation tooling (LangSmith, Langfuse, Ragas).
• Hands-on experience with Hugging Face Transformers, vLLM, Ollama, or fine-tuning techniques (LoRA / PEFT).
• Familiarity with MLOps infrastructure tools (MLflow, Kubeflow, Kubernetes, Terraform) or multi-GPU acceleration setups.
• Experience with asynchronous task queues and messaging brokers (Airflow, Redis, Celery, Kafka, or RabbitMQ).
• Knowledge of advanced graph retrieval (GraphRAG), knowledge graphs, recommendation engines, computer vision, or A/B experimentation platforms.