Senior AI/ML Engineer

Halcer; Indore District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Halcer seeks a Senior AI/ML Engineer to design, build, and deploy end-to-end ML, DL, and GenAI solutions. You will bridge cutting-edge AI research with production-grade engineering, applying NLP and CV techniques to unstructured data, while leading MLOps and cloud security practices for scalable deployment.

You will work with product and software teams to translate strategic goals into robust technical deliverables, using RAG, graph RAG, and PEFT-based model tuning where applicable.

Qualifications

• 5+ years hands-on AIML engineering deploying production-grade ML solutions.
• Strong foundation in calculus, linear algebra, and econometrics.
• Experience with RAG, Graph RAG, agentic workflows, and model tuning.
• Proficient in MLOps, CI/CD for ML, monitoring, and cloud security practices.
• Experience with AWS, GCP, or Azure.

Responsibilities

• Design, build, test, and deploy scalable ML/DL/GenAI pipelines that deliver business value.
• Architect and implement GenAI systems using RAG, Graph RAG, and agentic workflows.
• Apply NLP/CV architectures to unstructured data challenges.
• Lead MLOps, automated training/inference workflows, and cloud security compliance.
• Collaborate with product leads and engineers to translate goals into deliverables.

Skills

AIML engineering

Math & statistics

NLP/Computer Vision

MLOps

Cloud security

Tools

Docker

Kubernetes

Terraform

MLflow

Kubeflow

LangChain

LlamaIndex

Pinecone / Milvus / Qdrant

Neo4j

Job description

Role Overview

We are seeking a Senior AI/ML Engineer with a proven track record of designing, building, and deploying end-to-end Machine Learning, Deep Learning, and Generative AI solutions. In this role, you will bridge cutting-edge AI research and production-grade engineering-applying advanced statistical rigor, calculus, NLP, Computer Vision, and modern GenAI architectures to solve complex organizational problems. You will also lead MLOps practices and enforce cloud security standards to ensure scalable, high-throughput model deployment that generates clear business value.

Key Responsibilities

• Production AI/ML Engineering: Design, build, test, and deploy scalable Machine Learning, Deep Learning, and Generative AI pipelines that directly deliver demonstrable business value.
• Generative AI Systems: Architect and implement advanced GenAI systems utilizing Retrieval-Augmented Generation (RAG), Graph RAG, Agentic Workflows, and model tuning techniques.
• Domain Applications: Implement state-of-the-art Natural Language Processing (NLP) and/or Computer Vision (CV) architectures to solve complex unstructured data challenges.
• Statistical & Mathematical Modeling: Apply deep theoretical and practical knowledge of calculus, probability, and econometrics to formulate, validate, and optimize model performance.
• MLOps & Infrastructure: Spearhead CI/CD pipelines for ML, automated training/inference workflows, model monitoring, and cloud security compliance across enterprise environments.
• Technical Leadership: Work closely with product leads, software engineers, and business stakeholders to translate strategic goals into robust technical deliverables.

Required Qualifications & Experience

• Experience: 5+ years of hands-on experience in AIML engineering, with a track record of deploying production-grade Machine Learning solutions.
• Foundational Mathematics & Statistics: Strong theoretical and applied grounding in calculus, linear algebra, and econometric/statistical methods:
• Linear & Logistic Regression, Generalized Linear Models (GLM)
• Time Series Analysis, Survival Analysis, Sampling Techniques
• Dimension Reduction & Clustering: PCA, Factor Analysis, Multidimensional Scaling, Clustering
• Decision Trees: CART, CHAID, Discriminant Analysis
• Classical Machine Learning: Expert implementation of algorithms including Random Forest, Support Vector Machines (SVM), Gradient Boosting Machines (GBM), XGBoost, and ensemble approaches.
• Deep Learning Frameworks: Direct experience with CNNs, RNNs, and modern Transformer Architectures as applied to NLP and/or Computer Vision.
• Generative AI Stack: Direct engineering experience with:
• RAG (Retrieval-Augmented Generation) & Graph RAG
• Agentic Workflows & Multi-agent frameworks
• Model Fine-Tuning (PEFT, LoRA, instruction tuning)
• MLOps & Cloud Security: Extensive experience in automated ML deployment pipelines, continuous monitoring, containerization, and cloud security best practices (AWS, GCP, or Azure).

Technical Stack & Preferred Skills

• Languages & Frameworks: Python, PyTorch, TensorFlow, SQL, C++/CUDA (a plus).
• AI Tooling & Databases: Vector databases (e.g., Pinecone, Milvus, Qdrant), graph databases (Neo4j), and orchestration frameworks (LangChain, LlamaIndex).
• Infrastructure: Docker, Kubernetes, Terraform, MLflow, Kubeflow, or cloud-native MLOps tools.
• Soft Skills: Problem-solving mindset with a focus on business impact and clear technical communication.