Senior Artificial Intelligence Engineer

Futran Tech Solutions; India; Remote; INR 3,000,000 - 6,000,000; Full time

Job summary

Futran Tech Solutions seeks a highly skilled Senior AI Engineer to lead the design, development, and production deployment of advanced AI solutions. You will own end-to-end AI initiatives, including conversational AI, RAG pipelines, and scalable enterprise-grade applications.

You will collaborate with data engineering, backend, and DevOps teams, mentor junior engineers, and ensure robust monitoring, performance, and governance across models in production.

Qualifications

• Bachelor's or Master's in Computer Science, Data Science, or a related field.
• 5+ years hands-on experience in ML/AI engineering with production deployment.
• Proficiency in Python, Java, or C++ and ML/DL frameworks.

Responsibilities

• Design, train, and evaluate ML and DL models for classification, anomaly detection, forecasting, and NLP tasks.
• Architect Generative AI and RAG solutions for document search, conversational Q&A, and summarization.
• Implement vector stores and embeddings for context-aware responses.
• Fine-tune LLMs using SFT and PEFT (LoRA, QLoRA).
• Optimize models via quantization and deploy on cloud using Docker/Kubernetes with CI/CD.
• Monitor performance, drift, and retrain as needed.
• Collaborate with data engineering, backend, DevOps, and product teams; mentor engineers.

Skills

Python

Java

C++

TensorFlow

PyTorch

scikit-learn

pandas

NLP

Education

Bachelor's or Master's in Computer Science, Data Science, or related field

Tools

LangChain

LlamaIndex

FAISS

Pinecone

Azure AI Search

Docker

Kubernetes

SageMaker

Vertex AI

Spark

Hadoop

Airflow

Git

CI/CD

MLflow

Job description

Position Overview:

We are seeking a highly skilled, hands-onSenior AI Engineerto lead the design, development, and production deployment of advanced AI solutionsspanning traditional machine learning, deep learning, and Generative AI. You will own end-to-end AI initiatives, from architecture through optimization, deployment, and monitoring, and build scalable, enterprise-grade applications such as conversational AI assistants and Retrieval-Augmented Generation (RAG) pipelines that deliver measurable business value.

Key Responsibilities:

• Design, train, and evaluate ML and deep learning models (RNNs, GRUs, LSTMs, and Transformers such as BERT, T5, GPT) for classification, anomaly detection, forecasting, and NLP tasks.
• Architect and develop Generative AI and RAG solutions for document search, conversational Q&A, and summarization using frameworks like LangChain and LlamaIndex.
• Implement vector stores (e.g., FAISS, Pinecone, Azure AI Search), embeddings, and retrieval techniques for grounded, context‑aware responses.
• Optionally fine‑tune LLMs using SFT and PEFT methods (LoRA, QLoRA) on domain‑specific datasets.
• Optimize models via quantization (dynamic/static, INT8) to improve latency and reduce compute overhead.
• Deploy models into production on cloud platforms (AWS, Azure, GCP) using containerization (Docker, Kubernetes), collaborating with DevOps on CI/CD pipelines for scalability and reliability.
• Define and track technical and business metrics; monitor model drift and performance, and retrain as needed.
• Collaborate with cross‑functional teams (data engineering, backend, DevOps, product) and mentor junior engineers; write clean, reproducible, well‑documented code.

Required Qualifications:

• Bachelor's or Master's in Computer Science, Data Science, or a related field.
• 5+ yearsof hands‑on experience in machine learning, AI engineering, or data science, with proven production deployment experience.
• Proficiency in programming languages such as Python, Java, or C++.
• Strong understanding of deep learning frameworks (e.g., TensorFlow, PyTorch) and traditional machine learning algorithms, especially for sequence and NLP tasks.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Proficiency with ML/DL libraries (scikit‑learn, pandas) and Transformer models / open‑source LLMs (e.g., Hugging Face).
• Practical experience with GenAI tools, RAG frameworks, vector stores, and embeddings.
• Experience with model quantization, evaluation using statistical and business metrics, and production monitoring.
• Familiarity with MLflow and CI/CD practices.
• Excellent problem‑solving and communication skills; able to work independently and collaboratively.

Preferred qualifications:

• Experience fine‑tuning LLMs (SFT, LoRA, QLoRA) on domain‑specific datasets.
• Exposure to MLOps platforms (e.g., SageMaker, Vertex AI, Kubeflow).
• Familiarity with distributed data processing (e.g., Spark, Hadoop) and orchestration tools (e.g., Airflow).
• Experience building enterprise‑grade conversational or agentic AI solutions.
• Familiarity with computer vision and version control (Git).
• Contributions to research papers, blog posts, or open‑source projects in ML/NLP/GenAI.