Generative AI Engineer

Generative AI Engineer for one of our clients' MNCs.

Exp : 5+ years

Location : Remote

Job Type : 6 months contract + ext.

We're seeking a Generative AI Engineer to design, develop, fine-tune, and deploy advanced generative modelsranging from LLMs (like GPT), diffusion networks, to GANsfor cutting-edge applications across text, image, audio, and video domains. You'll help drive AI innovation from research to production, ensuring ethical and scalable AI solutions.

Key Responsibilities :

Model Design & Development :

- Architect and implement generative AI models (GANs, VAEs, Transformers, diffusion) for specific use cases

- Fine-tune pre-trained models and design new architectures informed by current research .

Data Pipeline & Management :

- Curate, preprocess, augment, and clean large datasets for training

- Handle structured and unstructured data; generate synthetic data as needed

Training, Testing & Optimization :

- Train models, experiment with hyperparameters, and optimize accuracy and throughput

- Conduct rigorous testinge.g., BLEU/FID scores, bias/fairness assessmentsand iterate accordingly

Deployment & Integration :

- Deploy models using MLOps, containerization, and cloud platforms (AWS, GCP, Azure, Kubernetes)

- Integrate generative APIs into products or services, working closely with engineering teams

Monitoring & Maintenance :

- Monitor production performance, detect model drift, and perform regular improvements .

- Optimize for scalability, inference speed, cost efficiency .

Collaboration & Communication :

- Partner with data scientists, software engineers, product, and design teams to align AI with business objectives

- Document models, pipelines, and results in clear technical and stakeholder-facing materials

Research & Innovation :

- Stay current with generative AI research; apply breakthroughs and prototype new ideas

- Lead proof-of-concepts, feasibility projects, and agentic AI or RAG pipelines

Ethical AI & Compliance :

- Address bias, fairness, privacy, and data governance through responsible model development

- Ensure compliance with regulations and institute safeguards against misuse

Mentorship & Standards (for senior roles) :

- Coach junior engineers, establish best practices/standards for generative AI workflows

Qualifications & Skills :

Education : Bachelor's or Masters in CS, AI, ML, Statistics, Math, or related field; PhD preferred for research positions .

Technical Skills :

- Expert in Python, PyTorch, TensorFlow, Hugging Face Transformers/Keras

- Deep understanding of generative models (GANs, VAEs, diffusion, LLMs)

- Familiarity with NLP, computer vision, reinforcement learning

- Proficient in MLOps frameworks, Docker/Kubernetes, cloud services

- Knowledge of RAG, vector databases (e.g., Pinecone, FAISS), prompt engineering

- Strong math foundationlinear algebra, calculus, probability/statistics

Soft Skills :

- Excellent problem-solving, critical thinking, and analytical skills.

- Strong written and verbal communication.

- Collaborative mindset and ability to engage with diverse stakeholders.

Preferred Experience :

- Experience building production-grade generative solutions: chatbots, content generators, AI agents

- Contributions to open-source projects or published AI research.

- Familiarity with CI/CD pipelines and agile workflows.

- Hands-on experience with RAG architectures, embeddings, vector search at scale

- Knowledge of AI ethics, responsible deployment, and compliance frameworks.

Required Skills & Experience :

- Proven 5+ yrs experience delivering RAG and Graph-RAG solutions in production.

- Strong proficiency in Python, vector DBs, and graph DB query languages (Cypher, Gremlin, SPARQL).

- Hands-on experience with LLMs, embedding models, and retrieval frameworks.

- Familiarity with MLOps tools (MLflow, Airflow, Docker, Kubernetes).

- Deep understanding of AI/ML/Data Science principles and practices.

- Conceptual knowledge of Agentic AI and autonomous agents (LangChain Agents, AutoGPT, CrewAI).

- Ability to clearly articulate past project experience, technical decisions, and outcomes