Senior Artificial Intelligence Engineer

SourcingXPress; Coimbatore District; On-site; INR 1,080,000 - 1,800,000; Full time

Job summary

SourcingXPress seeks a Senior AI/ML Engineer to design, build, and deploy AI-driven systems across the full ML lifecycle. You will work on data pipelines, model development, and deployment while collaborating with product, engineering, and client teams to deliver measurable business impact.

Responsibilities include integrating LLMs, building RAG pipelines, and exposing APIs with low latency; strong emphasis on MLOps, model evaluation, and governance.

Qualifications

• 5+ years of professional experience in ML, DL, or applied AI engineering.
• Strong Python proficiency and ML framework expertise (PyTorch, TF, scikit-learn).
• Hands-on with LLMs via APIs or self-hosted deployment.
• Experience with RAG, embeddings, and vector databases.
• Solid ML lifecycle knowledge: data prep, feature engineering, training, eval, deployment.
• Experience deploying models as REST/GraphQL services (FastAPI/Flask).
• Familiarity with cloud AI/ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
• Containerization & orchestration (Docker, Kubernetes) for deployment.
• MLOps tooling (MLflow, Weights & Biases) for experiment tracking.
• Strong prompt engineering, fine-tuning, and model eval techniques.
• Git-based collaboration and scalable development workflows.
• Analytical, problem-solving, and client-facing adaptability.

Responsibilities

• Design, develop, and deploy ML models and AI‑driven features for production.
• Build and maintain data pipelines for training, evaluation, and inference.
• Fine‑tune and integrate LLMs and foundation models into client products.
• Design RAG pipelines, vector search, and prompt strategies.
• Develop model inference APIs with low latency and scalability.
• Collaborate with backend/mobile teams to integrate AI into platforms.
• Establish MLOps with versioning, CI/CD, and monitoring for drift.
• Evaluate outputs for accuracy, bias, and safety; implement guardrails.
• Optimize performance, cost, and compute across clouds.
• Stay current with AI research and tool adoption for client value.
• Document architecture, experiments, and decisions for knowledge sharing.

Skills

Python

PyTorch

TensorFlow

scikit‑learn

LLMs APIs

RAG architectures

Embeddings/VectorDBs

REST/GraphQL services

Prompt engineering

Git

ML lifecycle

Model evaluation

Cloud platforms

MLOps tooling

Tools

Docker

Kubernetes

MLflow

Weights & Biases

FastAPI

Flask

Pinecone

FAISS

Weaviate

pgvector

LangChain

LlamaIndex

Job description

Job Description

We are looking for a Senior AI/ML Engineer with 5+ years of hands‑on experience designing, building, and deploying machine learning and AI‑driven systems in production. You will work across the full ML lifecycle from data pipelines and model development to deployment and monitoring partnering closely with product, engineering, and client teams to deliver intelligent solutions that create measurable business impact.

Salary Range

₹ 5-18 Lacs PA

Key Responsibilities

• Design, develop, and deploy machine learning models and AI‑driven features for production applications.
• Build and maintain data pipelines for training, evaluation, and inference, ensuring data quality and reproducibility.
• Fine‑tune and integrate large language models (LLMs) and other foundation models into client‑facing products.
• Design and implement retrieval‑augmented generation (RAG) pipelines, vector search, and prompt engineering strategies.
• Develop and expose model inference APIs, ensuring low latency, scalability, and reliability.
• Collaborate with backend and mobile engineering teams to integrate AI capabilities into existing platforms.
• Establish MLOps practices, including model versioning, CI/CD for ML, and automated monitoring for drift and performance degradation.
• Evaluate model outputs for accuracy, bias, and safety, and implement guardrails where required.
• Optimize model performance, inference cost, and compute utilization across cloud environments.
• Stay current with emerging AI/ML research and tooling, and recommend adoption where it adds client value.
• Document architecture, experiments, and model decisions to support internal knowledge sharing and client delivery.

Required Skills & Qualifications

• 5+ years of professional experience in machine learning, deep learning, or applied AI engineering.
• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or scikit‑learn.
• Hands‑on experience working with LLMs (OpenAI, Anthropic, open‑source models) via APIs or self‑hosted deployment.
• Practical experience with RAG architectures, embeddings, and vector databases (e.g., Pinecone, Weaviate, FAISS, pgvector).
• Solid understanding of the ML lifecycle: data preprocessing, feature engineering, training, evaluation, and deployment.
• Experience deploying models as REST/GraphQL services using frameworks such as FastAPI or Flask.
• Familiarity with cloud AI/ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI).
• Working knowledge of containerization and orchestration (Docker, Kubernetes) for model deployment.
• Experience with MLOps tooling such as MLflow, Weights & Biases, or similar for experiment tracking.
• Strong understanding of prompt engineering, fine‑tuning, and model evaluation techniques.
• Proficiency with Git‑based version control and collaborative development workflows.
• Strong analytical and problem‑solving skills, with the ability to work independently in a client‑facing, fast‑paced environment.

Good to Have

• Experience with agentic AI frameworks (LangChain, LlamaIndex, or similar).
• Exposure to fine‑tuning open‑source LLMs and parameter‑efficient techniques (LoRA, QLoRA).
• Familiarity with data engineering tools such as Airflow, Spark, or Kafka.
• Understanding of AI governance, responsible AI practices, and data privacy considerations.
• Prior experience in an AI consulting or client‑delivery environment, managing multiple concurrent projects.
• Exposure to fintech, mobile, or cloud‑platform domains.