Artificial Intelligence Engineer

engineo solutions; Delhi; On-site; INR 900,000 - 1,500,000; Full time

Job summary

engineo solutions is seeking an AI Engineer to own ML and generative AI capabilities across the delivery lifecycle. The role focuses on turning ambiguous requirements into testable AI solutions and shipping reliable capabilities.

You will collaborate with senior engineers and domain experts to ensure maintainable, secure, and measurable outcomes. The position emphasizes practical software engineering, data preparation, model training, evaluation, and deployment in a production environment, with

Qualifications

• Strong Python programming and software engineering fundamentals.
• Experience with ML concepts and data preparation.
• Familiarity with PyTorch, TensorFlow, scikit-learn.

Responsibilities

• Build and maintain ML or generative AI solutions addressing product problems.
• Prepare and validate datasets with reproducible pipelines.
• Develop evaluation approaches for model quality.
• Deploy AI services via APIs and orchestration workflows.
• Monitor production models and identify drift or failures.
• Collaborate with product and engineering stakeholders.
• Contribute production-quality code with tests and documentation.
• Write maintainable production code in Python.
• Understand responsible AI practices and data privacy.

Skills

Python

ML concepts

Data handling

Problem solving

Tools

PyTorch

TensorFlow

scikit-learn

SQL

Docker

Kubernetes

Job description

We are a technology-driven organization focused on building practical products and services that solve meaningful customer and business problems. Our teams combine software engineering, data, and product thinking to turn complex requirements into reliable solutions. We value ownership, learning, thoughtful execution, and measurable outcomes. Engineers work closely with product, design, operations, and business stakeholders to understand problems clearly and deliver improvements that customers can experience. We are building an environment where early-career professionals can develop strong technical judgment, learn from experienced teammates, and contribute to production systems. The organization encourages experimentation when it is grounded in evidence, disciplined engineering practices, and responsible use of technology. By joining our engineering team, you will help strengthen intelligent capabilities while developing the practical skills required to move models and ideas from experimentation into dependable business value.

About the Role

As an AI Engineer, you will own the development and improvement of machine learning and generative AI capabilities that support business and customer outcomes. You will work across the delivery lifecycle: understanding the problem, preparing data, developing models or AI workflows, evaluating quality, integrating services, and monitoring results in production. The role is designed for an engineer with 1–3 years of experience who can combine practical software development with curiosity about modern AI techniques. You will partner with senior engineers, product stakeholders, and domain experts to convert ambiguous requirements into testable solutions. Success means shipping reliable capabilities, improving model or workflow performance, and making AI features maintainable for the wider engineering team. You will also contribute to engineering standards, documentation, and responsible practices that help the organization scale AI adoption with confidence.

Key Responsibilities

• Build, integrate, and maintain machine learning or generative AI solutions that address defined product and business problems, improving user experience, process efficiency, or decision quality.
• Prepare, transform, and validate datasets through reproducible pipelines, ensuring data quality, traceability, and fit-for-purpose inputs for model development and evaluation.
• Develop evaluation approaches using relevant metrics, test cases, and human or business feedback, turning results into targeted improvements in model quality and system reliability.
• Deploy AI services through maintainable APIs, applications, or orchestration workflows, working with platform and software engineers to support secure, scalable production usage.
• Monitor model and application behavior after release, identifying drift, latency, cost, failures, or quality degradation and driving corrective actions through measurable operational signals.
• Translate stakeholder requirements into technical designs, communicating trade-offs clearly and keeping delivery aligned with expected outcomes, constraints, and responsible AI considerations.
• Contribute production-quality code through version control, peer reviews, automated testing, documentation, and incident learning, strengthening the team’s engineering standards over time.
• Strong Python programming and practical software engineering fundamentals, including data structures, APIs, testing, version control, debugging, and writing maintainable production code.
• Working knowledge of machine learning concepts, model training and evaluation, data preparation, and common libraries such as PyTorch, TensorFlow, scikit-learn, or equivalent tools.
• Familiarity with SQL, cloud or containerized environments, and modern AI patterns such as embeddings, vector search, prompt design, retrieval-augmented generation, or model-serving workflows.
• Ability to analyze experimental results, communicate technical findings clearly, and collaborate with product and engineering stakeholders to turn uncertain problems into measurable delivery plans.
• Understanding of responsible AI practices, including data privacy, bias awareness, security, reproducibility, and monitoring requirements for systems used by real customers or business teams.

Additional Plus

• Experience taking a machine learning, NLP, computer vision, recommendation, or generative AI prototype into a usable application or production workflow.
• Exposure to MLOps or DevOps practices such as CI/CD, experiment tracking, model registries, observability, Docker, Kubernetes, or managed cloud AI services.
• Familiarity with LLM evaluation, fine-tuning, agent workflows, prompt versioning, or techniques for improving inference quality, latency, and cost.

What You'll Bring

You bring a practical engineering mindset and the ability to learn quickly in a changing technical environment. You can write clear Python, reason about data and model behavior, and work methodically from an unclear problem toward a testable solution. You are comfortable asking questions, validating assumptions, and using evidence rather than relying on intuition alone. You understand that an AI capability is valuable only when it is reliable, maintainable, secure, and connected to a real user or business outcome. You take responsibility for the quality of your work, including tests, documentation, monitoring, and follow-through after release. You collaborate openly with senior engineers and cross-functional partners, explaining technical concepts in language that supports sound decisions. You are curious about new developments in machine learning and generative AI, but you also know when a simpler approach is more appropriate. You will be successful if you combine technical fundamentals, disciplined execution, thoughtful communication, and a strong willingness to grow through feedback and hands-on delivery.

Why Join Us

• Build practical AI capabilities that move beyond experimentation, with ownership across data, engineering, evaluation, deployment, and measurable business impact.
• Work alongside experienced engineers and cross-functional partners, gaining exposure to production systems, responsible AI decisions, and the full lifecycle of intelligent products.
• Grow through meaningful ownership, structured feedback, and opportunities to deepen both core software engineering skills and modern machine learning expertise.
• Join a collaborative engineering environment that values learning, clear communication, dependable delivery, and solutions that create lasting value for customers and the organization.

What We Offer

• A hands-on opportunity to work on real AI engineering problems across experimentation, integration, deployment, evaluation, and production improvement.
• Direct collaboration with engineering, product, and business teams, providing broad exposure to how technical systems create customer and operational outcomes.
• A learning-oriented environment with mentorship, peer feedback, and space to build depth in machine learning, generative AI, software engineering, and MLOps practices.
• An onsite role in Gurugram with clear ownership, meaningful work, and the opportunity to contribute to an engineering organization scaling practical AI adoption.