AI Engineer - Experience in building AI agents at scale and implementing robust AI evaluation - CTC INR 50 L

Job Description :

We are seeking a highly skilled and motivated AI Engineer with expertise in large language models (LLMs), AI workflows, and machine learning. This role combines deep technical knowledge in ML / AI with hands-on experience building intelligent, production-ready systems that enhance cybersecurity investigation, prioritization, and response. You will work at the intersection of LLM-driven automation, workflow orchestration, and classical ML models to improve how alerts are prioritized, classified, and contextualized—reducing fatigue and enabling faster, more effective decision-making.

Your work will directly influence the development of agentic AI systems, workflow automation, and recommendation engines within cloud security platform.

Key Responsibilities

LLM Integration & Workflows :

Build, fine-tune, and integrate large language models (LLMs) into existing systems.

Develop agentic workflows for investigation, classification, and automated response in cybersecurity.

Apply techniques like retrieval-augmented generation (RAG), prompt engineering, and fine-tuning for domain-specific tasks.

Machine Learning Development :

Design, implement, and optimize ML models for prioritization, ranking, clustering, anomaly detection, and classification.

Apply both classical forecasting models (AR, ARIMA, SARIMA, ES) and modern architectures (XGBoost, LSTM, DeepAR, N-BEATS, Temporal Fusion Transformer).

Data Preparation & Feature Engineering :

Collect, preprocess, and transform structured and unstructured data (including logs, text, and access patterns).

Engineer features to maximize model interpretability and performance.

Model Training, Evaluation, and Deployment :

Train and evaluate models using rigorous metrics (precision, recall, AUC, F1, etc.).

Optimize hyperparameters and fine-tune LLMs for task-specific improvements.

Deploy ML / LLM models into production at scale with strong monitoring, drift detection, and observability.

Collaboration & Documentation :

Work closely with data scientists, ML engineers, security researchers, and software teams to build end-to-end solutions.

Document models, workflows, and pipelines for clarity, reproducibility, and knowledge sharing.

Requirements

Bachelor’s / Master’s degree in Computer Science, AI / ML, Data Science, or a related field.

5+ years of experience in ML / AI, including 3+ years deploying production-grade systems.

Experience contributing to publications (patents, libraries, or peer-reviewed papers) is a plus.

Strong knowledge of machine learning algorithms for classification, clustering, ranking, and anomaly detection.

Proficiency with LLM frameworks and APIs (OpenAI, Hugging Face Transformers, LangChain, LlamaIndex).

Hands-on experience building workflow automation with LLMs and integrating them into applications.

Solid programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn).

Knowledge of NLP tasks (text classification, summarization, embeddings, semantic search).

Experience with recommendation systems or reinforcement learning is a strong plus.

Proven track record of deploying ML / AI models into production environments with scalability in mind.

Familiarity with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes).

Understanding of MLOps best practices (CI / CD for ML, monitoring, retraining strategies).

Strong problem-solving and analytical mindset.

Excellent communication and teamwork skills.

Ability to work in a fast-paced, evolving startup environment.

Write to me at for more details.