AI/ML Engineer

Experience & Educational Requirements:

• Minimum of 7 years relevant experience in AI/ML engineering, data science or related roles (industry or product‑engineering).
• Bachelor's or Master's degree in Computer Science, Information Technology or similar.
• Strong preference: B.Tech/M.Tech in CSE/IT or equivalent and good depth in data structures, algorithms, mathematics for ML.
• Demonstrable track record of building and deploying ML models in production (for at least 3–5 years within the 7+).
• Experience in product‑oriented work (versus purely research) is advantageous.

Key Responsibilities:

• Lead the design, development, implementation and optimization of machine learning, deep learning and anomaly‑detection models to power Tranzmeo's flagship product (e.g., "T‑Connect OneView") and other AI solutions for industrial/IoT infrastructure.
• Work end‑to‑end: Data ingestion, cleaning, feature engineering, model training, evaluation, deployment (edge/cloud/hybrid) and monitoring.
• Collaborate with hardware/edge teams (e.g., fiber‑optic sensing, DAS/DTS data streams) to integrate signal / sensor data pipelines, enabling real‑time/near‑real‑time anomaly detection.
• Build scalable architectures for AI/ML workflows – covering large volumes of streaming data, distributed processing, possibly edge‑computing constraints.
• Mentor junior ML/data engineers, review code, design best practices, enforce engineering discipline (unit‑testable code, model versioning, reproducibility).
• Engage with cross‑functional stakeholders (product management, domain experts in oil & gas/infrastructure, field engineering) to translate business or operational problems into ML solutions, and iterate model outcomes.
• Keep abreast of latest developments in ML/AI (especially anomaly detection, streaming analytics, sensor‑signal processing, edge ML) and bring actionable innovation into the company's tech stack.
• Contribute to technical architecture decisions, choosing frameworks/libraries/tools (e.g., Python ecosystem, TensorFlow/PyTorch, streaming frameworks, cloud/edge deployment).
• Measure model performance in production, define KPIs for detection/false‑alarm rates, maintain model drift tracking, identify when to retrain/refine.
• Document designs, maintain clear version control, ensure reproducibility of experiments, and manage model lifecycle.
• Assist in patenting, IP documentation or presenting internal/external publications/workshops (given the company's deep‑tech orientation).

Required Technical Skills.

• Excellent proficiency in Python (including libraries such as NumPy, Pandas, Scikit‑Learn, etc.).
• Strong programming skills: algorithmic thinking, data structures, software engineering fundamentals, version control (Git), code reviews.
• Deep experience in ML/AI: supervised, unsupervised, anomaly detection, time‑series, streaming data, deep learning (CNNs, RNNs/LSTM, transformers if applicable).
• Experience with sensor data, large scale data ingestion/processing pipelines, streaming analytics (e.g., Kafka, Spark Streaming, Flink) is a plus.
• Knowledge of deployment of ML models: either cloud (AWS, Azure, GCP) or edge/hybrid, model serving frameworks, monitoring tools.
• Familiarity with IoT/industrial data domains (e.g., pipelines, remote sensing, fiber‑optic sensors) is highly desirable – domain knowledge will accelerate impact.
• Strong mathematics/statistics background: probability, linear algebra, optimization, hypothesis testing, performance metrics ROC/AUC etc.
• Ability to write clean, maintainable engineering code (not just notebooks) and collaborate with software / embedded teams.
• Good communication skills for liaising with non‑ML stakeholders, explaining model outcomes, presenting results.

Desired Additional Skills / Attributes

• Experience with anomaly detection frameworks and techniques specific to industrial systems, remote sensing, intrusion detection.
• Experience with big data technologies (Hadoop/Spark, NoSQL, time‑series databases) and large scale datasets (millions of records)
• Familiarity with edge computing constraints: model size, latency, hardware sensors, embedded systems.
• Intellectual curiosity, startup mindset: able to work in a growing, somewhat lean organisation, comfortable with ambiguity, taking initiative. This aligns well with Tranzmeo's culture ("entrepreneurial outlook", "independent thinking")
• Leadership / mentoring ability: able to guide junior engineers, elevate team's capability.
• Strong problem‑solver: able to pick up domain knowledge rapidly (oil & gas/infrastructure), translate business problem into ML pipeline.
• Good documentation, reproducibility mindset, focus on production readiness (not just prototyping).
• Willingness to travel or work with field data, deploy models in industrial settings if needed.

Job Type: Full-time

Pay: ₹700, ₹1,975,540.57 per year

Work Location: In person